Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genetic and phenotypic landscape of the major histocompatibilty complex region in the Japanese population

Abstract

To perform detailed fine-mapping of the major-histocompatibility-complex region, we conducted next-generation sequencing (NGS)-based typing of the 33 human leukocyte antigen (HLA) genes in 1,120 individuals of Japanese ancestry, providing a high-resolution allele catalog and linkage-disequilibrium structure of both classical and nonclassical HLA genes. Together with population-specific deep-whole-genome-sequencing data (n = 1,276), we conducted NGS-based HLA, single-nucleotide-variant and indel imputation of large-scale genome-wide-association-study data from 166,190 Japanese individuals. A phenome-wide association study assessing 106 clinical phenotypes identified abundant, significant genotype–phenotype associations across 52 phenotypes. Fine-mapping highlighted multiple association patterns conferring independent risks from classical HLA genes. Region-wide heritability estimates and genetic-correlation network analysis elucidated the polygenic architecture shared across the phenotypes.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: High-resolution allele-frequency spectra and linkage disequilibrium of HLA genes.
Fig. 2: Machine-learning-based clustering of haplotypes by using HLA allele information.
Fig. 3: Genotype–phenotype association patterns identified by PheWAS with NGS-based HLA, SNV and indel imputation.
Fig. 4: Matrix plot of gene and phenotype associations in the entire MHC region.
Fig. 5: Region-wide heritability and genetic-correlation networks across phenotypes.

Code availability

Software and codes used for this study are available from URLs or upon request to the authors.

Data availability

HLA data have been deposited at the National Bioscience Database Center (NBDC) Human Database (research ID: hum0114) as open data without any access restrictions. GWAS data and phenotype data of the BBJ individuals are available at the NBDC Human Database (research ID: hum0014).

References

  1. 1.

    Dendrou, C. A., Petersen, J., Rossjohn, J. & Fugger, L. HLA variation and disease. Nat. Rev. Immunol. 18, 325–339 (2018).

    CAS  Article  Google Scholar 

  2. 2.

    Okada, Y. et al. HLA-Cw*1202-B*5201-DRB1*1502 haplotype increases risk for ulcerative colitis but reduces risk for Crohn’s disease. Gastroenterology 141, 864–871 (2011).

    CAS  Article  Google Scholar 

  3. 3.

    Okada, Y. et al. Risk for ACPA-positive rheumatoid arthritis is driven by shared HLA amino acid polymorphisms in Asian and European populations. Hum. Mol. Genet. 23, 6916–6926 (2014).

    CAS  Article  Google Scholar 

  4. 4.

    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).

    CAS  Article  Google Scholar 

  5. 5.

    Robinson, J., Soormally, A. R., Hayhurst, J. D. & Marsh, S. G. The IPD-IMGT/HLA Database: new developments in reporting HLA variation. Hum. Immunol. 77, 233–237 (2016).

  6. 6.

    The MHC sequencing consortium. Complete sequence and gene map of a human major histocompatibility complex. Nature 401, 921–923 (1999).

    Article  Google Scholar 

  7. 7.

    Horton, R. et al. Gene map of the extended human MHC. Nat. Rev. Genet. 5, 889–899 (2004).

    CAS  Article  Google Scholar 

  8. 8.

    Nejentsev, S. et al. Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A. Nature 450, 887–892 (2007).

    CAS  Article  Google Scholar 

  9. 9.

    Jia, X. et al. Imputing amino acid polymorphisms in human leukocyte antigens. PLoS One 8, e64683 (2013).

    CAS  Article  Google Scholar 

  10. 10.

    Raychaudhuri, S. et al. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat. Genet. 44, 291–296 (2012).

    CAS  Article  Google Scholar 

  11. 11.

    Okada, Y. et al. Fine mapping major histocompatibility complex associations in psoriasis and its clinical subtypes. Am. J. Hum. Genet. 95, 162–172 (2014).

    CAS  Article  Google Scholar 

  12. 12.

    Hirata, J. et al. Variants at HLA-A, HLA-C, and HLA-DQB1 confer risk of psoriasis vulgaris in Japanese. J. Invest. Dermatol. 138, 542–548 (2018).

    CAS  Article  Google Scholar 

  13. 13.

    Hosomichi, K., Shiina, T., Tajima, A. & Inoue, I. The impact of next-generation sequencing technologies on HLA research. J. Hum. Genet. 60, 665–673 (2015).

    CAS  Article  Google Scholar 

  14. 14.

    Zhou, F. et al. Deep sequencing of the MHC region in the Chinese population contributes to studies of complex disease. Nat. Genet. 48, 740–746 (2016).

    CAS  Article  Google Scholar 

  15. 15.

    Robinson, J. et al. Distinguishing functional polymorphism from random variation in the sequences of >10,000 HLA-A, -B and -C alleles. PLoS Genet. 13, e1006862 (2017).

    Article  Google Scholar 

  16. 16.

    Schofl, G. et al. 2.7 million samples genotyped for HLA by next generation sequencing: lessons learned. BMC Genomics 18, 161 (2017).

    Article  Google Scholar 

  17. 17.

    Walter, K. et al. The UK10K project identifies rare variants in health and disease. Nature 526, 82–90 (2015).

    CAS  Article  Google Scholar 

  18. 18.

    Okada, Y. et al. Contribution of a nonclassical HLA gene, HLA-DOA, to the risk of rheumatoid arthritis. Am. J. Hum. Genet. 99, 366–374 (2016).

    CAS  Article  Google Scholar 

  19. 19.

    Okushi, Y. et al. Circulating and renal expression of HLA-G prevented chronic renal allograft dysfunction in Japanese recipients. Clin. Exp. Nephrol. 21, 932–940 (2017).

    CAS  Article  Google Scholar 

  20. 20.

    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).

    CAS  Article  Google Scholar 

  21. 21.

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

  22. 22.

    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).

    Article  Google Scholar 

  23. 23.

    Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).

    CAS  Article  Google Scholar 

  24. 24.

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

    CAS  Article  Google Scholar 

  25. 25.

    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).

    CAS  Article  Google Scholar 

  26. 26.

    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).

    CAS  Article  Google Scholar 

  27. 27.

    Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1110 (2013).

    CAS  Article  Google Scholar 

  28. 28.

    Bush, W. S., Oetjens, M. T. & Crawford, D. C. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet. 17, 129–145 (2016).

    CAS  Article  Google Scholar 

  29. 29.

    Karnes, J. H. et al. Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants. Sci. Transl. Med. 9, eaai8708 (2017).

    Article  Google Scholar 

  30. 30.

    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 

  31. 31.

    Hosomichi, K. et al. Phase-defined complete sequencing of the HLA genes by next-generation sequencing. BMC Genomics 14, 355 (2013).

    CAS  Article  Google Scholar 

  32. 32.

    Hosomichi, K., Mitsunaga, S., Nagasaki, H. & Inoue, I. A bead-based normalization for uniform sequencing depth (BeNUS) protocol for multi-samples sequencing exemplified by HLA-B. BMC Genomics 15, 645 (2014).

    Article  Google Scholar 

  33. 33.

    Yang, K. L. et al. New allele name of some HLA-DRB1*1401: HLA-DRB1*1454. Int. J. Immunogenet. 36, 119–120 (2009).

    CAS  Article  Google Scholar 

  34. 34.

    Morizane, A. et al. MHC matching improves engraftment of iPSC-derived neurons in non-human primates. Nat. Commun. 8, 385 (2017).

    Article  Google Scholar 

  35. 35.

    van der Maaten, L. & Hilton, G. Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  36. 36.

    van der Maaten, L. Visualizing data using t-SNE. J. Mach. Learn. Res. 15, 3221–3245 (2014).

    Google Scholar 

  37. 37.

    Ester, M., Kriegel, H. P., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. in KDD ‘96 Proc. Second Int. Conf. Knowl. Discov. Data Min., 226–231 (AAAI Press, Palo Alto, CA, USA, 1996).

  38. 38.

    Bauer, D. C., Zadoorian, A., Wilson, L. O. W. & Thorne, N. P. Evaluation of computational programs to predict HLA genotypes from genomic sequencing data. Brief. Bioinform. 19, 179–187 (2018).

    CAS  PubMed  Google Scholar 

  39. 39.

    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).

    CAS  Article  Google Scholar 

  40. 40.

    Nishida, N. et al. Understanding of HLA-conferred susceptibility to chronic hepatitis B infection requires HLA genotyping-based association analysis. Sci. Rep. 6, 24767 (2016).

    CAS  Article  Google Scholar 

  41. 41.

    Okada, Y. et al. Identification of nine novel loci associated with white blood cell subtypes in a Japanese population. PLoS Genet. 7, e1002067 (2011).

    CAS  Article  Google Scholar 

  42. 42.

    Ikeshita, S., Miyatake, Y., Otsuka, N. & Kasahara, M. MICA/B expression in macrophage foam cells infiltrating atherosclerotic plaques. Exp. Mol. Pathol. 97, 171–175 (2014).

    CAS  Article  Google Scholar 

  43. 43.

    Hu, X. et al. Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk. Nat. Genet. 47, 898–905 (2015).

    CAS  Article  Google Scholar 

  44. 44.

    Roe, D. et al. Revealing complete complex KIR haplotypes phased by long-read sequencing technology. Genes Immun. 18, 127–134 (2017).

    CAS  Article  Google Scholar 

  45. 45.

    See, P. et al. Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356, eaag3009 (2017).

    Article  Google Scholar 

  46. 46.

    Takeuchi, Y. et al. Clinical response to PD-1 blockade correlates with a sub-fraction of peripheral central memory CD4+ T cells in patients with malignant melanoma. Int. Immunol. 30, 13–22 (2018).

    CAS  Article  Google Scholar 

  47. 47.

    Platzer, A. Visualization of SNPs with t-SNE. PLoS One 8, e56883 (2013).

    CAS  Article  Google Scholar 

  48. 48.

    Shi, H., Mancuso, N., Spendlove, S. & Pasaniuc, B. Local genetic correlation gives insights into the shared genetic architecture of complex traits. Am. J. Hum. Genet. 101, 737–751 (2017).

    CAS  Article  Google Scholar 

  49. 49.

    Szolek, A. et al. OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30, 3310–3316 (2014).

    CAS  Article  Google Scholar 

  50. 50.

    Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152–1158 (2015).

    CAS  Article  Google Scholar 

  51. 51.

    Kawaguchi, S. et al. HLA-HD: an accurate HLA typing algorithm for next-generation sequencing data. Hum. Mutat. 38, 788–797 (2017).

    CAS  Article  Google Scholar 

  52. 52.

    Lee, H. & Kingsford, C. Kourami: graph-guided assembly for novel human leukocyte antigen allele discovery. Genome. Biol. 19, 16 (2018).

    Article  Google Scholar 

  53. 53.

    Nothnagel, M. & Rohde, K. The effect of single-nucleotide polymorphism marker selection on patterns of haplotype blocks and haplotype frequency estimates. Am. J. Hum. Genet. 77, 988–998 (2005).

    CAS  Article  Google Scholar 

  54. 54.

    Okada, Y. eLD: entropy-based linkage disequilibrium index between multi-allelic sites. Hum. Genome Var. 5, 29 (2018).

    Article  Google Scholar 

  55. 55.

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

    CAS  Article  Google Scholar 

  56. 56.

    Karnes, J. H. et al. Comparison of HLA allelic imputation programs. PLoS One 12, e0172444 (2017).

    Article  Google Scholar 

  57. 57.

    Lenz, T. L. et al. Widespread non-additive and interaction effects within HLA loci modulate the risk of autoimmune diseases. Nat. Genet. 47, 1085–1090 (2015).

    CAS  Article  Google Scholar 

  58. 58.

    Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  Article  Google Scholar 

  59. 59.

    Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011).

    Article  Google Scholar 

  60. 60.

    Speed, D. et al. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986–992 (2017).

    CAS  Article  Google Scholar 

  61. 61.

    Hinks, A. et al. Fine-mapping the MHC locus in juvenile idiopathic arthritis (JIA) reveals genetic heterogeneity corresponding to distinct adult inflammatory arthritic diseases. Ann. Rheum. Dis. 76, 765–772 (2017).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank T. Aoi for kind support of the study. 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), the Japan Agency for Medical Research and Development (AMED), MEXT KAKENHI (221S0002), Bioinformatics Initiative of Osaka University Graduate School of Medicine, and Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University. Y.O. was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (15H05670, 15H05911 and 15K14429), AMED (18gm6010001h0003 and 18ek0410041h0002), Takeda Science Foundation, the Uehara Memorial Foundation, the Naito Foundation, Daiichi Sankyo Foundation of Life Science, Senri Life Science Foundation and Suzuken Memorial Foundation. K.H. was supported by JSPS KAKENHI grant no. P16H06502 ‘Neo-self’. J.H. is an employee of Teijin Pharma Limited. Computations were partially performed on the NIG supercomputer at ROIS National Institute of Genetics.

Author information

Affiliations

Authors

Contributions

Y.O. supervised the study. J.H., K.H., Y.K. and Y.O. wrote the manuscript. J.H., K.H., S.S., M. Kanai, K.I., K.S., M.A., T.K., K.O., T.M., K.Y., Y.K. and Y.O. conducted data analysis. M.H., K.M., M. Kubo and Y.K. provided data. Y.O., K.M. and M. Kubo collected samples. Y.O., K.H., H.N., Y.M. and I.I. conducted experiments.

Corresponding author

Correspondence to Yukinori Okada.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1, 2 and 4–7, and Supplementary Figures 1–3

Reporting Summary

Supplementary Tables

Supplementary Tables 3 and 8

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hirata, J., Hosomichi, K., Sakaue, S. et al. Genetic and phenotypic landscape of the major histocompatibilty complex region in the Japanese population. Nat Genet 51, 470–480 (2019). https://doi.org/10.1038/s41588-018-0336-0

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing