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Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population

Abstract

To understand the genetics of type 2 diabetes in people of Japanese ancestry, we conducted A meta-analysis of four genome-wide association studies (GWAS; 36,614 cases and 155,150 controls of Japanese ancestry). We identified 88 type 2 diabetes–associated loci (P < 5.0 × 10−8) with 115 independent signals (P < 5.0 × 10−6), of which 28 loci with 30 signals were novel. Twenty-eight missense variants were in linkage disequilibrium (r2 > 0.6) with the lead variants. Among the 28 missense variants, three previously unreported variants had distinct minor allele frequency (MAF) spectra between people of Japanese and European ancestry (MAFJPN > 0.05 versus MAFEUR < 0.01), including missense variants in genes related to pancreatic acinar cells (GP2) and insulin secretion (GLP1R). Transethnic comparisons of the molecular pathways identified from the GWAS results highlight both ethnically shared and heterogeneous effects of a series of pathways on type 2 diabetes (for example, monogenic diabetes and beta cells).

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Fig. 1: Manhattan plot of meta-analysis.
Fig. 2: Regional association plots and the structure of GLP1R.
Fig. 3: Overlap between type 2 diabetes signals and the lead cis-eQTL variants of the GTEx database.

Data availability

GWAS summary statistics of type 2 diabetes will be publicly available at our website (JENGER, http://jenger.riken.jp/en/) and the National Bioscience Database Center (NBDC, https://humandbs.biosciencedbc.jp/en/) Human Database. Genotype data of case samples are available at NBDC under research ID hum0014.

References

  1. Stumvoll, M., Goldstein, B. J. & van Haeften, T. W. Type 2 diabetes: pathogenesis and treatment. Lancet 371, 2153–2156 (2008).

    Article  Google Scholar 

  2. Scott, R. A. et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66, 2888–2902 (2017).

    Article  CAS  Google Scholar 

  3. Huxley, R. et al. Ethnic comparisons of the cross-sectional relationships between measures of body size with diabetes and hypertension. Obes. Rev. 9 (Suppl. 1), 53–61 (2008).

    Article  Google Scholar 

  4. 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).

    Article  CAS  Google Scholar 

  5. 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).

    Article  CAS  Google Scholar 

  6. Hara, K. et al. Genome-wide association study identifies three novel loci for type 2 diabetes. Hum. Mol. Genet. 23, 239–246 (2014).

    Article  CAS  Google Scholar 

  7. Imamura, M. et al. Genome-wide association studies in the Japanese population identify seven novel loci for type 2 diabetes. Nat. Commun. 7, 10531 (2016).

    Article  CAS  Google Scholar 

  8. Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  9. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  Google Scholar 

  10. Zhao, W. et al. Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease. Nat. Genet. 49, 1450–1457 (2017).

    Article  CAS  Google Scholar 

  11. Mahajan, A. et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 50, 559–571 (2018).

    Article  CAS  Google Scholar 

  12. Bonàs-Guarch, S. et al. Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes. Nat. Commun. 9, 321 (2018).

    Article  Google Scholar 

  13. Kanai, M., Tanaka, T. & Okada, Y. Empirical estimation of genome-wide significance thresholds based on the 1000 Genomes Project data set. J. Hum. Genet. 61, 861–866 (2016).

    Article  CAS  Google Scholar 

  14. Sveinbjornsson, G. et al. Weighting sequence variants based on their annotation increases power of whole-genome association studies. Nat. Genet. 48, 314–317 (2016).

    Article  CAS  Google Scholar 

  15. Grarup, N. et al. Identification of novel high-impact recessively inherited type 2 diabetes risk variants in the Greenlandic population. Diabetologia 61, 2005–2015 (2018).

    Article  CAS  Google Scholar 

  16. Pearce, L. R. et al. KSR2 mutations are associated with obesity, insulin resistance, and impaired cellular fuel oxidation. Cell 155, 765–777 (2013).

    Article  CAS  Google Scholar 

  17. Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

    Article  CAS  Google Scholar 

  18. Zhou, Q. et al. A multipotent progenitor domain guides pancreatic organogenesis. Dev. Cell 13, 103–114 (2007).

    Article  CAS  Google Scholar 

  19. Cogger, K. F. et al. Glycoprotein 2 is a specific cell surface marker of human pancreatic progenitors. Nat. Commun. 8, 331 (2017).

    Article  Google Scholar 

  20. Logsdon, C. D. & Ji, B. The role of protein synthesis and digestive enzymes in acinar cell injury. Nat. Rev. Gastroenterol. Hepatol. 10, 362–370 (2013).

    CAS  Google Scholar 

  21. Zhou, Q., Brown, J., Kanarek, A., Rajagopal, J. & Melton, D. A. In vivo reprogramming of adult pancreatic exocrine cells to β-cells. Nature 455, 627–632 (2008).

    Article  CAS  Google Scholar 

  22. The GTEx Consortium. et al. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  23. Muraro, M. J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394.e3 (2016).

    Article  CAS  Google Scholar 

  24. Voight, B. F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010).

    Article  CAS  Google Scholar 

  25. Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    Article  CAS  Google Scholar 

  26. Wessel, J. et al. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat. Commun. 6, 5897 (2015).

    Article  CAS  Google Scholar 

  27. Mahajan, A. et al. Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus. PLoS Genet. 11, e1004876 (2015).

    Article  Google Scholar 

  28. Scott, R. A. et al. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. Sci. Transl. Med. 8, 341ra76 (2016).

    Article  Google Scholar 

  29. Jazayeri, A. et al. Crystal structure of the GLP-1 receptor bound to a peptide agonist. Nature 546, 254–258 (2017).

    Article  CAS  Google Scholar 

  30. Sathananthan, A. et al. Common genetic variation in GLP1R and insulin secretion in response to exogenous GLP-1 in nondiabetic subjects: a pilot study. Diabetes Care 33, 2074–2076 (2010).

    Article  CAS  Google Scholar 

  31. Seino, Y. et al. Pharmacodynamics of the glucagon-like peptide-1 receptor agonist lixisenatide in Japanese and Caucasian patients with type 2 diabetes mellitus poorly controlled on sulphonylureas with/without metformin. Diabetes Obes. Metab. 16, 739–747 (2014).

    Article  CAS  Google Scholar 

  32. Ward, L. D. & Kellis, M. HaploRegv4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 44, 877–881 (2016).

    Article  Google Scholar 

  33. Harrison, K. D. et al. Nogo-B receptor is necessary for cellular dolichol biosynthesis and protein N-glycosylation. EMBO J. 30, 2490–2500 (2011).

    Article  CAS  Google Scholar 

  34. Harrison, K. D. et al. Nogo-B receptor stabilizes Niemann-Pick type C2 protein and regulates intracellular cholesterol trafficking. Cell Metab. 10, 208–218 (2009).

    Article  CAS  Google Scholar 

  35. Park, E. J. et al. Mutation of Nogo-B receptor, a subunit of cis-prenyltransferase, causes a congenital disorder of glycosylation. Cell Metab. 20, 448–457 (2014).

    Article  CAS  Google Scholar 

  36. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  Google Scholar 

  37. Creyghton, M. P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl Acad. Sci. USA 107, 21931–21936 (2010).

    Article  CAS  Google Scholar 

  38. Heintzman, N. D. et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat. Genet. 39, 311–318 (2007).

    Article  CAS  Google Scholar 

  39. Akiyama, M. et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat. Genet. 49, 1458–1467 (2017).

    Article  CAS  Google Scholar 

  40. Kanai, M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet. 50, 390–400 (2018).

    Article  CAS  Google Scholar 

  41. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  Google Scholar 

  42. Kobashi, G. et al. High body mass index after age 20 and diabetes mellitus are independent risk factors for ossification of the posterior longitudinal ligament of the spine in Japanese subjects: a case-control study in multiple hospitals. Spine 29, 1006–1010 (2004).

    Article  Google Scholar 

  43. Nakanishi, N., Yoshida, H., Matsuo, Y., Suzuki, K. & Tatara, K. White blood-cell count and the risk of impaired fasting glucose or type II diabetes in middle-aged Japanese men. Diabetologia 45, 42–48 (2002).

    Article  CAS  Google Scholar 

  44. American Diabetes Association. Classification and diagnosis of diabetes: standards of medical care in diabetes. Diabetes Care 41 (Suppl. 1), S13–S27 (2018).

  45. Xue, A. et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9, 2941 (2018).

    Article  Google Scholar 

  46. Kwak, S. H. et al. Nonsynonymous variants in PAX4 and GLP1R are associated with Type 2 diabetes in an East Asian population. Diabetes 67, 1892–1902 (2018).

    Article  CAS  Google Scholar 

  47. Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2–S8 (2017).

    Article  Google Scholar 

  48. Seino, Y. et al. Report of the committee on the classification and diagnostic criteria of diabetes mellitus. Diabetol. Int. 1, 212–228 (2010).

    Article  Google Scholar 

  49. Kuriyama, S. et al. The Tohoku Medical Megabank Project: design and mission. J. Epidemiol. 26, 493–511 (2016).

    Article  Google Scholar 

  50. Tsugane, S. et al. The JPHC study: design and some findings on the typical Japanese diet. Jpn. J. Clin. Oncol. 44, 777–782 (2014).

    Article  Google Scholar 

  51. Hamajima, N. et al. The Japan Multi-Institutional Collaborative Cohort Study (J-MICC Study) to detect gene-environment interactions for cancer. Asian Pac. J. Cancer Prev. 8, 317–323 (2007).

    PubMed  Google Scholar 

  52. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  Google Scholar 

  53. Okada, Y. et al. Deep whole-genome sequencing reveals recent selection signatures linked to evolution and disease risk of Japanese. Nat. Commun. 9, 1631 (2018).

    Article  Google Scholar 

  54. Ogura, Y. et al. A functional SNP in BNC2 is associated with adolescent idiopathic scoliosis. Am. J. Hum. Genet. 97, 337–342 (2015).

    Article  CAS  Google Scholar 

  55. Hirota, T. et al. Genome-wide association study identifies eight new susceptibility loci for atopic dermatitis in the Japanese population. Nat. Genet. 44, 1222–1226 (2012).

    Article  CAS  Google Scholar 

  56. Nakajima et al. A genome-wide association study identifies susceptibility loci for ossification of the posterior longitudinal ligament of the spine. Nat. Genet. 46, 1012–1016 (2014).

    Article  CAS  Google Scholar 

  57. Okada, Y., Raj, T. & Yamamoto, K. Ethnically shared and heterogeneous impacts of molecular pathways suggested by the genome-wide meta-analysis of rheumatoid arthritis. Rheumatology 55, 186–189 (2015).

    Article  Google Scholar 

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Acknowledgements

We acknowledge the staff of the BBJ Project, IMM, ToMMo, the JPHC Study and the J-MICC Study for their outstanding assistance in collecting samples and clinical information. We also acknowledge the members of the Genetic Study Group of the Investigation Committee on the Ossification of Spinal Ligaments for recruiting subjects to the ossification of the posterior longitudinal ligament GWAS used in this study, which was supported by the Japan Agency for Medical Research and Development (AMED) (17ek0109223h0001) (S.I.). The study of psychiatric disorders was supported by AMED under grants JP18dm0107097 (N.I., M. Ikeda and K.Y.), JP18km0405201 (N.I.) and JP18km0405208 (M. Ikeda). We are grateful to members of The Rotary Club of Osaka-Midosuji District 2660 Rotary International in Japan for supporting our study. This research was funded by the Tailor-Made Medical Treatment Program (the BBJ Project) of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) and AMED (M. Kubo and Y.M.). This research was also supported by the Advanced Genome Research and Bioinformatics Study to Facilitate Medical Innovation (GRIFIN) in the Platform Program for Promotion of Genome Medicine (P3GM) of AMED (T.K.), JP18km0405202. IMM is supported by MEXT and AMED (grants JP18km0105003 and JP18km0105004, M.S.) ToMMo is supported by MEXT and AMED (grants JP18km0105001, JP18km0105002, JP18km0405203 and JP18km0405001, M.Y.). The JPHC Study has been supported by the National Cancer Center Research and Development Fund since 2011 and was supported by a Grant-in-Aid for Cancer Research from the Ministry of Health, Labour and Welfare of Japan from 1989 to 2010 (S.T.). The J-MICC Study was supported by Grants-in-Aid for Scientific Research for Priority Areas of Cancer (17015018) and Innovative Areas (221S0001) and the JSPS KAKENHI Grant (16H06277) from MEXT (A.K., K.K., M.N. and K.W.).

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K.S., M.A., M. Horikoshi and Y.K. designed the study and wrote the manuscript. K.S., M.A., M. Horikoshi, K.I. and M. Kanai performed statistical analysis. J.H., N. Shojima, A.H., A.K., K.K., M.N., K.T., Y.I., M. Hirata, K.M., N.I., M. Ikeda, N. Sawada, T. Yamaji, M. Iwasaki, S.I., S.M., Y.M., K.W., S.T., M.S., M.Y. and Y.O. contributed to data acquisition. M. Horikoshi, Y.K., M. Kubo, T. Yamauchi and T.K. supervised the study. All authors contributed to and approved the final version of the manuscript.

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Correspondence to Yoichiro Kamatani, Momoko Horikoshi, Toshimasa Yamauchi or Takashi Kadowaki.

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Integrated supplementary information

Supplementary Figure 1 Overview of the study design of genome-wide association studies for type 2 diabetes in Japanese.

The study participants consist of 36,614 Japanese type 2 diabetes subjects from the BioBank Japan (BBJ) Project and 155,150 Japanese controls from the BBJ Project, Iwate Tohoku Medical Megabank Organization (IMM), Tohoku Medical Megabank Organization (ToMMo), the Japan Public Health Center–based Prospective (JPHC) Study, the Japan Multi-institutional Collaborative Cohort (J-MICC) Study, Osaka-Midousuji Rotary Club, and Pharma SNP Consortium.

Supplementary Figure 2 Principal component analysis (PCA) of the subjects recruited for the present study (GWAS3 and GWAS4; n=191,764).

Cases and controls are colored red and orange, respectively. HapMap samples of Japanese in Tokyo (JPT; n = 44) and Han Chinese in Beijing (CHB; n = 45) are colored blue and cyan, respectively. Subjects in participating cohorts that were not used in the present study are colored gray.

Supplementary Figure 3 Q–Q plot of the meta-analysis of type 2 diabetes in Japanese.

Association P-values on imputed genotype data for 12,557,761 variants are plotted.

Supplementary Figure 4 Histogram of minor-allele frequency (MAF) of the identified 88 type 2 diabetes lead variants (P < 5.0 × 10–8) in Japanese and Europeans.

MAF is divided into three bins: common (MAF ≥ 0.05), low-frequency (0.05 > MAF ≥ 0.01) and rare (MAF < 0.01). Novel loci are colored in red, and reported loci are colored in blue. The numbers of type 2 diabetes lead variants in each category is shown above the bar graph. The number of novel type 2 diabetes lead variants in each category is shown in parentheses.

Supplementary Figure 5 Scatter plot of the effect sizes of the lead variants identified in the Japanese type 2 diabetes GWAS in Japanese and European populations.

Effect sizes of the lead variants at 69 type 2 diabetes loci (P < 5.0 × 10−8) in Japanese (x-axis) and European (y-axis) populations are plotted. Of the 88 type 2 diabetes loci identified in the Japanese type 2 diabetes GWAS (n = 191,764), effect sizes in the European type 2 diabetes GWAS (Scott, R. A. et al. Diabetes 66, 2888–2902 (2017); n = 159,208)2 were available for 69 loci. Lead variants in novel and established loci are colored in red and blue, respectively. Error bars indicate 95% confidence interval.

Supplementary Figure 6 Scatter plot of the effect sizes of the lead variants reported in the European type 2 diabetes GWAS in Japanese and European populations.

Effect sizes of the lead variants in the Japanese (x-axis) and European (y-axis) populations are plotted. a, Of the 113 lead variants reported in Scott, R. A. et al. Diabetes 66, 2888–2902 (2017) (n = 159,208)2, effect sizes in the Japanese type 2 diabetes GWAS (n = 191,764) were available for 95 loci. b, Of the 231 lead variants reported in Mahajan, A. et al. Nat. Genet. 50, 1505–1513 (2018) (n = 898,130)17, effect sizes in the Japanese type 2 diabetes GWAS were available for 192 loci. Error bars indicate 95% confidence interval. Effect sizes of the primary signals of the type 2 diabetes loci unadjusted for body mass index are included in the analysis.

Supplementary Figure 7 Number of independent signals (P < 5.0 × 10–6) in the 88 type 2 diabetes loci identified in Japanese.

Distribution of the number of independent type 2 diabetes signals in the 88 type 2 diabetes loci is represented in a pie chart.

Supplementary Figure 8 Gene expression of GP2 and CPA1.

a,b, Gene expression of GP2 (a) and CPA1 (b) in the 53 tissues reported by Genotype Tissue Expression (GTEx) database. Expression values are shown in Transcripts Per Kilobase Million mapped reads (TPM). Box plots are shown as median, 25th, and 75th percentiles.

Supplementary Figure 9 Heritability enrichment of the ten cell-type groups and 220 cell types.

a,b, Heritability enrichment in the 10 cell-type groups (a) and the 220 cell-types (b) for the type 2 diabetes GWAS in the Japanese population was estimated via stratified LD score regression. The black dashed lines are the cutoff for Bonferroni significance.

Supplementary Figure 10 Genome-wide genetic correlation between type 2 diabetes and other human complex traits in Japanese.

Genetic correlation (rg) and corresponding standard error (error bars) between type 2 diabetes and the traits displayed on the y-axis were estimated using bivariate linkage-disequilibrium score regression. The genetic correlation estimates (rg) are colored based on their values and direction (red for positive and blue for negative correlation). FDR-q values were calculated by the Benjamini–Hochberg method. Of the 91 human complex traits (32 diseases and 59 quantitative traits) analyzed, 15 traits showing significant (FDR-q < 0.01) genetic correlation with type 2 diabetes are displayed.

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Suzuki, K., Akiyama, M., Ishigaki, K. et al. Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population. Nat Genet 51, 379–386 (2019). https://doi.org/10.1038/s41588-018-0332-4

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