Abstract

Recent evidence suggests that a substantial portion of complex disease risk alleles modify gene expression in a cell-specific manner1,2,3,4. To identify candidate causal genes and biological pathways of immune-related complex diseases, we conducted expression quantitative trait loci (eQTL) analysis on five subsets of immune cells (CD4+ T cells, CD8+ T cells, B cells, natural killer (NK) cells and monocytes) and unfractionated peripheral blood from 105 healthy Japanese volunteers. We developed a three-step analytical pipeline comprising (i) prediction of individual gene expression using our eQTL database and public epigenomic data, (ii) gene-level association analysis and (iii) prediction of cell-specific pathway activity by integrating the direction of eQTL effects. By applying this pipeline to rheumatoid arthritis data sets, we identified candidate causal genes and a cytokine pathway (upregulation of tumor necrosis factor (TNF) in CD4+ T cells). Our approach is an efficient way to characterize the polygenic contributions and potential biological mechanisms of complex diseases.

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Acknowledgements

We would like to thank all the doctors and staff who participated in sample collection for eQTL analysis and the BioBank Japan Project and staff at the Laboratory for Genotyping Development. This research was supported by funding from Takeda pharmaceutical Co., Ltd. (Y. Kochi, K.F. and K. Yamamoto), and a grant from RIKEN (K. Ishigaki, Y. Kochi, A.S., Y.M., Y. Kamatani and M.K.). The BioBank Japan Project is supported by the Japanese Ministry of Education, Culture, Sports, Sciences and Technology.

Author information

Affiliations

  1. Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.

    • Kazuyoshi Ishigaki
    • , Yuta Kochi
    • , Akari Suzuki
    • , Kensuke Yamaguchi
    • , Yukinori Okada
    • , Ryo Yamada
    •  & Kazuhiko Yamamoto
  2. Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.

    • Kazuyoshi Ishigaki
    • , Yumi Tsuchida
    • , Haruka Tsuchiya
    • , Shuji Sumitomo
    • , Kensuke Yamaguchi
    • , Yasuo Nagafuchi
    • , Shinichiro Nakachi
    • , Rika Kato
    • , Keiichi Sakurai
    • , Hirofumi Shoda
    • , Keishi Fujio
    •  & Kazuhiko Yamamoto
  3. Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.

    • Kazuyoshi Ishigaki
    •  & Yoichiro Kamatani
  4. CREST, Japan Science and Technology Agency, Tokyo, Japan.

    • Yuta Kochi
    • , Katsunori Ikari
    • , Fuyuki Miya
    •  & Tatsuhiko Tsunoda
  5. Institute of Rheumatology, Tokyo Women's Medical University, Tokyo, Japan.

    • Katsunori Ikari
    • , Atsuo Taniguchi
    •  & Hisashi Yamanaka
  6. Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.

    • Fuyuki Miya
    •  & Tatsuhiko Tsunoda
  7. Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.

    • Fuyuki Miya
    •  & Tatsuhiko Tsunoda
  8. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.

    • Yukinori Okada
  9. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.

    • Yukinori Okada
  10. Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.

    • Yukihide Momozawa
    •  & Michiaki Kubo
  11. Statistical Genetics, Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.

    • Ryo Yamada

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Contributions

K. Ishigaki., Y. Kochi., A.S., K.F. and K. Yamamoto designed the research project. K. Ishigaki conducted bioinformatics analysis with the help of Y. Kamatani, F.M., T.T. and K. Yamaguchi. A.S., Y.M. and M.K. performed RNA sequencing. K. Ikari, A.T. and H.Y. contributed samples and data for the IORRA cohort. Y.T., H.T., S.S., Y.N., S.N., R.K., K.S. and H.S. contributed samples and data for eQTL analysis. K. Ishigaki wrote the manuscript with critical input from Y. Kochi, K.F., Y.O. and R.Y.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Yuta Kochi.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–17 and Supplementary Tables 1 and 14

Excel files

  1. 1.

    Supplementary Table 2

    Enrichment of cell-specific eQTL variants within transcription factor binding sites.

  2. 2.

    Supplementary Table 3

    List of candidate causal genes identified by combining GWAS catalog and eQTL data of each cell type.

  3. 3.

    Supplementary Table 4

    List of candidate causal genes identified by combining GWAS catalog and exon-level eQTL data of each cell type.

  4. 4.

    Supplementary Table 5

    List of candidate causal genes identified by combining GWAS catalog and TSS-conditioned eQTL data of each cell type.

  5. 5.

    Supplementary Table 6

    Bayesian test for colocalisation between GWAS variants of RA and eQTL variants of each cell type.

  6. 6.

    Supplementary Table 7

    eQTL variants and their effect sizes used to predict gene expression of CD4+ T cells.

  7. 7.

    Supplementary Table 8

    eQTL variants and their effect sizes used to predict gene expression of CD8+ T cells.

  8. 8.

    Supplementary Table 9

    eQTL variants and their effect sizes used to predict gene expression of B cells.

  9. 9.

    Supplementary Table 10

    eQTL variants and their effect sizes used to predict gene expression of NK cells.

  10. 10.

    Supplementary Table 11

    eQTL variants and their effect sizes used to predict gene expression of monocytes.

  11. 11.

    Supplementary Table 12

    eQTL variants and their effect sizes used to predict gene expression of PB.

  12. 12.

    Supplementary Table 13

    Genes with Bonferroni significance in the case-control analysis using predicted gene expression.

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DOI

https://doi.org/10.1038/ng.3885

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