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

Nature Genetics volume 51, pages 379–386 (2019)Cite this article

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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.

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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.

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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.).

Author information

Author notes

  1. These authors contributed equally: M. Horikoshi, Y. Kamatani.

Authors and Affiliations

  1. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Ken Suzuki, Jun Hosoe, Nobuhiro Shojima, Toshimasa Yamauchi & Takashi Kadowaki

  2. Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Ken Suzuki, Masato Akiyama, Kazuyoshi Ishigaki, Masahiro Kanai, Yukinori Okada & Yoichiro Kamatani

  3. Laboratory for Endocrinology, Metabolism and Kidney Diseases, RIKEN Centre for Integrative Medical Sciences, Yokohama, Japan

    Ken Suzuki, Shiro Maeda & Momoko Horikoshi

  4. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan

    Ken Suzuki & Yukinori Okada

  5. Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

    Masato Akiyama

  6. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Masahiro Kanai

  7. Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan

    Atsushi Hozawa & Masayuki Yamamoto

  8. Graduate School of Medicine, Tohoku University, Sendai, Japan

    Atsushi Hozawa & Masayuki Yamamoto

  9. Center for Epidemiologic Research in Asia, Shiga University of Medical Science, Otsu, Japan

    Aya Kadota

  10. Department of Public Health, Shiga University of Medical Science, Otsu, Japan

    Aya Kadota

  11. Laboratory of Public Health, School of Food and Nutritional Sciences, University of Shizuoka, Shizuoka, Japan

    Kiyonori Kuriki

  12. Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan

    Mariko Naito & Kenji Wakai

  13. Department of Oral Epidemiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan

    Mariko Naito

  14. Division of Clinical Research and Epidemiology, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Iwate, Japan

    Kozo Tanno

  15. Department of Hygiene and Preventive Medicine, School of Medicine, Iwate Medical University, Iwate, Japan

    Kozo Tanno

  16. Division of Innovation & Education, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Iwate, Japan

    Yasushi Ishigaki

  17. Division of Diabetes, Metabolism and Endocrinology, Department of Internal Medicine, Iwate Medical University, Iwate, Japan

    Yasushi Ishigaki

  18. Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Makoto Hirata

  19. Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Koichi Matsuda

  20. Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan

    Nakao Iwata & Masashi Ikeda

  21. Division of Epidemiology, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan

    Norie Sawada, Taiki Yamaji & Motoki Iwasaki

  22. Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan

    Shiro Ikegawa

  23. Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan

    Shiro Maeda

  24. Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara, Japan

    Shiro Maeda

  25. Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yoshinori Murakami

  26. Center for Public Health Sciences, National Cancer Center, Tokyo, Japan

    Shoichiro Tsugane

  27. Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Iwate, Japan

    Makoto Sasaki

  28. Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, Iwate, Japan

    Makoto Sasaki

  29. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan

    Yukinori Okada

  30. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Michiaki Kubo

  31. Kyoto-McGill International Collaborative School in Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan

    Yoichiro Kamatani

  32. Department of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Takashi Kadowaki

  33. Department of Metabolism and Nutrition, Mizonokuchi Hospital, Faculty of Medicine, Teikyo University, Tokyo, Japan

    Takashi Kadowaki

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  1. Ken Suzuki
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Contributions

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.

Corresponding authors

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

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Competing interests

The authors declare no competing interests.

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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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