Clinical measurements can be viewed as useful intermediate phenotypes to promote understanding of complex human diseases. To acquire comprehensive insights into the underlying genetics, here we conducted a genome-wide association study (GWAS) of 58 quantitative traits in 162,255 Japanese individuals. Overall, we identified 1,407 trait-associated loci (P < 5.0 × 10−8), 679 of which were novel. By incorporating 32 additional GWAS results for complex diseases and traits in Japanese individuals, we further highlighted pleiotropy, genetic correlations, and cell-type specificity across quantitative traits and diseases, which substantially expands the current understanding of the associated genetics and biology. This study identified both shared polygenic effects and cell-type specificity, represented by the genetic links among clinical measurements, complex diseases, and relevant cell types. Our findings demonstrate that even without prior biological knowledge of cross-phenotype relationships, genetics corresponding to clinical measurements successfully recapture those measurements’ relevance to diseases, and thus can contribute to the elucidation of unknown etiology and pathogenesis.

  • Subscribe to Nature Genetics for full access:



Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

  2. 2.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

  3. 3.

    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).

  4. 4.

    Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

  5. 5.

    Surakka, I. et al. The impact of low-frequency and rare variants on lipid levels. Nat. Genet. 47, 589–597 (2015).

  6. 6.

    Okada, Y. et al. Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations. Nat. Genet. 44, 904–909 (2012).

  7. 7.

    Pattaro, C. et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat. Commun. 7, 10023 (2016).

  8. 8.

    Kamatani, Y. et al. Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat. Genet. 42, 210–215 (2010).

  9. 9.

    Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429 (2016).

  10. 10.

    Surendran, P. et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat. Genet. 48, 1151–1161 (2016).

  11. 11.

    Liu, C. et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat. Genet. 48, 1162–1170 (2016).

  12. 12.

    Ehret, G. B. et al. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat. Genet. 48, 1171–1184 (2016).

  13. 13.

    Sivakumaran, S. et al. Abundant pleiotropy in human complex diseases and traits. Am. J. Hum. Genet. 89, 607–618 (2011).

  14. 14.

    Han, B. et al. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nat. Genet. 48, 803–810 (2016).

  15. 15.

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

  16. 16.

    Lee, S. H., Yang, J., Goddard, M. E., Visscher, P. M. & Wray, N. R. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics 28, 2540–2542 (2012).

  17. 17.

    Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014).

  18. 18.

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

  19. 19.

    Hirata, M. et al. Cross-sectional analysis of BioBank Japan clinical data: a large cohort of 200,000 patients with 47 common diseases. J. Epidemiol. 27, S9–S21 (2017).

  20. 20.

    1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  21. 21.

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

  22. 22.

    Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

  23. 23.

    Tsai, W.-N. et al. Serum total bilirubin concentrations are inversely associated with total white blood cell counts in an adult population. Ann. Clin. Biochem. 52, 251–258 (2015).

  24. 24.

    Liu, Y. et al. Bilirubin possesses powerful immunomodulatory activity and suppresses experimental autoimmune encephalomyelitis. J. Immunol. 181, 1887–1897 (2008).

  25. 25.

    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).

  26. 26.

    Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

  27. 27.

    Okada, Y. et al. Construction of a population-specific HLA imputation reference panel and its application to Graves’ disease risk in Japanese. Nat. Genet. 47, 798–802 (2015).

  28. 28.

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

  29. 29.

    Ikeda, M. et al. A genome-wide association study identifies two novel susceptibility loci and trans population polygenicity associated with bipolar disorder. Mol. Psychiatry https://doi.org/10.1038/mp.2016.259 (2017).

  30. 30.

    Low, S.-K. et al. Identification of six new genetic loci associated with atrial fibrillation in the Japanese population. Nat. Genet. 49, 953–958 (2017).

  31. 31.

    Liu, J., Au Yeung, S. L., Lin, S. L., Leung, G. M. & Schooling, C. M. Liver enzymes and risk of ischemic heart disease and type 2 diabetes mellitus: a Mendelian randomization study. Sci. Rep. 6, 38813 (2016).

  32. 32.

    Rosenberg, M. A. et al. Genetic variants related to height and risk of atrial fibrillation: the cardiovascular health study. Am. J. Epidemiol. 180, 215–222 (2014).

  33. 33.

    Khankari, N. K. et al. Association between adult height and risk of colorectal, lung, and prostate cancer: results from meta-analyses of prospective studies and Mendelian randomization analyses. PLoS Med. 13, e1002118 (2016).

  34. 34.

    Wu, A. H., Gladden, J. D., Ahmed, M., Ahmed, A. & Filippatos, G. Relation of serum uric acid to cardiovascular disease. Int. J. Cardiol. 213, 4–7 (2016).

  35. 35.

    Azab, B. et al. Value of albumin-globulin ratio as a predictor of all-cause mortality after non-ST elevation myocardial infarction. Angiology 64, 137–145 (2013).

  36. 36.

    Perlstein, T. S., Pande, R. L., Beckman, J. A. & Creager, M. A. Serum total bilirubin level and prevalent lower-extremity peripheral arterial disease: National Health and Nutrition Examination Survey (NHANES) 1999 to 2004. Arterioscler. Thromb. Vasc. Biol. 28, 166–172 (2008).

  37. 37.

    Timio, F., Kerry, S. M., Anson, K. M., Eastwood, J. B. & Cappuccio, F. P. Calcium urolithiasis, blood pressure and salt intake. Blood Press. 12, 122–127 (2003).

  38. 38.

    Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

  39. 39.

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

  40. 40.

    Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

  41. 41.

    Trynka, G. et al. Disentangling the effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complex-trait loci. Am. J. Hum. Genet. 97, 139–152 (2015).

  42. 42.

    Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  43. 43.

    Sidney, L. E., Branch, M. J., Dunphy, S. E., Dua, H. S. & Hopkinson, A. Concise review: evidence for CD34 as a common marker for diverse progenitors. Stem Cells 32, 1380–1389 (2014).

  44. 44.

    Ziegler-Heitbrock, H. W. L. & Ulevitch, R. J. CD14: cell surface receptor and differentiation marker. Immunol. Today 14, 121–125 (1993).

  45. 45.

    Gadhoum, S. Z. & Sackstein, R. CD15 expression in human myeloid cell differentiation is regulated by sialidase activity. Nat. Chem. Biol. 4, 751–757 (2008).

  46. 46.

    Clark, E. A. & Lane, P. J. L. Regulation of human B-cell activation and adhesion. Annu. Rev. Immunol. 9, 97–127 (1991).

  47. 47.

    Sakaguchi, S., Sakaguchi, N., Asano, M., Itoh, M. & Toda, M. Immunologic self-tolerance maintained by activated T cells expressing IL-2 receptor alpha-chains (CD25). Breakdown of a single mechanism of self-tolerance causes various autoimmune diseases. J. Immunol. 155, 1151–1164 (1995).

  48. 48.

    Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

  49. 49.

    International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

  50. 50.

    Li, Y., Willer, C. J., Ding, J., Scheet, P. & Abecasis, G. R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).

  51. 51.

    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).

  52. 52.

    Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

  53. 53.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

  54. 54.

    Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

  55. 55.

    Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

  56. 56.

    Hirata, J. et al. Variants at HLA-A, HLA-C, and HLA-DQB1 confer risk of psoriasis vulgaris in Japanese. J. Invest. Dermatol. https://doi.org/10.1016/j.jid.2017.10.001 (2017).

  57. 57.

    Yavorska, O. O. & Burgess, S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int. J. Epidemiol. 46, 1734–1739 (2017).

Download references


We acknowledge the staff of BBJ for their outstanding assistance in collecting samples and clinical information. We also thank the Tohoku Medical Megabank Project, the Japan Public Health Center–based Prospective (JPHC) Study, and the Japan Multi-Institutional Collaborative Cohort (J-MICC) Study for their invaluable contributions to the case-control studies used in this study. We thank the staff of the Japan Scoliosis Clinical Research Group (JSCRG) for their support in recruiting patients to the AIS GWAS used in this study. We are grateful to H. Finucane for helpful discussions and assistance with LD score regression analysis. This research was supported by the Tailor-Made Medical Treatment Program (the BioBank Japan Project) of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) and the Japan Agency for Medical Research and Development (AMED). The study of psychiatric disorders was supported by the Strategic Research Program for Brain Sciences (SRPBS) of AMED. M. Kanai was supported by a Nakajima Foundation Fellowship. Y.O. was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (grants 15H05670, 15H05907, 15H05911, 15K14429, 16H03269, and 16K15738), AMED (grants 16km0405206h0001, 16gm6010001h0001, and 17ek0410041h0001), Takeda Science Foundation, the Uehara Memorial Foundation, the Naito Foundation, Daiichi Sankyo Foundation of Life Science, and Senri Life Science Foundation.

Author information

Author notes

  1. These authors jointly supervised this work: Yukinori Okada and Yoichiro Kamatani.


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

    • Masahiro Kanai
    •  & Yukinori Okada
  2. Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    • Masahiro Kanai
    • , Masato Akiyama
    • , Atsushi Takahashi
    • , Nana Matoba
    • , Yukinori Okada
    •  & Yoichiro Kamatani
  3. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    • Masahiro Kanai
  4. Department of Genomic Medicine, Research Institute, National Cerebral and Cardiovascular Center, Osaka, Japan

    • Atsushi Takahashi
  5. Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    • Yukihide Momozawa
  6. Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan

    • Masashi Ikeda
    •  & Nakao Iwata
  7. Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan

    • Shiro Ikegawa
  8. Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    • Makoto Hirata
  9. Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    • Koichi Matsuda
  10. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    • Michiaki Kubo
  11. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan

    • Yukinori Okada
  12. Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan

    • Yoichiro Kamatani


  1. Search for Masahiro Kanai in:

  2. Search for Masato Akiyama in:

  3. Search for Atsushi Takahashi in:

  4. Search for Nana Matoba in:

  5. Search for Yukihide Momozawa in:

  6. Search for Masashi Ikeda in:

  7. Search for Nakao Iwata in:

  8. Search for Shiro Ikegawa in:

  9. Search for Makoto Hirata in:

  10. Search for Koichi Matsuda in:

  11. Search for Michiaki Kubo in:

  12. Search for Yukinori Okada in:

  13. Search for Yoichiro Kamatani in:


M. Kanai, M.A., M. Kubo, Y.O., and Y.K. designed the study and wrote the manuscript. K.M., M.H., and M. Kubo collected and managed the BBJ samples. Y.M. and M. Kubo performed genotyping. M. Kanai, M.A., A.T., and N.M. performed statistical analysis. S.I., M.I., and N.I. contributed to data acquisition. Y.O. and Y.K. supervised the study. All authors contributed to and approved the final version of the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Yukinori Okada or Yoichiro Kamatani.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–9 and Supplementary Note 1

  2. Life Sciences Reporting Summary

  3. Supplementary Tables

    Supplementary Tables 1–12

  4. Supplementary Dataset 1

    Manhattan, quantile–quantile, and LD score plots for the 58 quantitative traits

  5. Supplementary Dataset 2

    Regional plots for all identified trait-associated loci