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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

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

  2. 2.

    et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).

  3. 3.

    et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

  4. 4.

    et al. Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nat. Genet. 44, 502–510 (2012).

  5. 5.

    et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

  6. 6.

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

  7. 7.

    et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

  8. 8.

    et al. Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins. Nat. Genet. 47, 88–91 (2015).

  9. 9.

    et al. Origin of monocytes and macrophages in a committed progenitor. Nat. Immunol. 14, 821–830 (2013).

  10. 10.

    & Roles of BCL6 in normal and transformed germinal center B cells. Immunol. Rev. 247, 172–183 (2012).

  11. 11.

    et al. Accelerated apoptosis of peripheral blood monocytes in Cebpb-deficient mice. Biochem. Biophys. Res. Commun. 464, 654–658 (2015).

  12. 12.

    , , & A statistical framework for joint eQTL analysis in multiple tissues. PLoS Genet. 9, e1003486 (2013).

  13. 13.

    et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 325, 1246–1250 (2009).

  14. 14.

    et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 6, e1000895 (2010).

  15. 15.

    et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat. Genet. 47, 1457–1464 (2015).

  16. 16.

    et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

  17. 17.

    et al. Genome-wide association analysis identifies new susceptibility loci for Behçet's disease and epistasis between HLA-B*51 and ERAP1. Nat. Genet. 45, 202–207 (2013).

  18. 18.

    et al. CCR2 deficiency impairs macrophage infiltration and improves cognitive function after traumatic brain injury. J. Neurotrauma 31, 1677–1688 (2014).

  19. 19.

    et al. CCR2-dependent dendritic cell accumulation in the central nervous system during early effector experimental autoimmune encephalomyelitis is essential for effector T cell restimulation in situ and disease progression. J. Immunol. 194, 531–541 (2015).

  20. 20.

    , , , & Monocyte and macrophage differentiation: circulation inflammatory monocyte as biomarker for inflammatory diseases. Biomark. Res. 2, 1 (2014).

  21. 21.

    et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

  22. 22.

    , & The role of innate and adaptive immunity in Parkinson's disease. J. Parkinsons Dis. 3, 493–514 (2013).

  23. 23.

    et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214–219 (2011).

  24. 24.

    et al. Dendritic cell-associated lectin-1: a novel dendritic cell-associated, C-type lectin-like molecule enhances T cell secretion of IL-4. J. Immunol. 169, 5638–5648 (2002).

  25. 25.

    et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

  26. 26.

    et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

  27. 27.

    et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

  28. 28.

    , & Cytokines in rheumatoid arthritis—shaping the immunological landscape. Nat. Rev. Rheumatol. 12, 63–68 (2016).

  29. 29.

    & Cytokines in the pathogenesis of rheumatoid arthritis. Nat. Rev. Immunol. 7, 429–442 (2007).

  30. 30.

    et al. A trial of etanercept, a recombinant tumor necrosis factor receptor:Fc fusion protein, in patients with rheumatoid arthritis receiving methotrexate. N. Engl. J. Med. 340, 253–259 (1999).

  31. 31.

    et al. Head-to-head comparison of subcutaneous abatacept versus adalimumab for rheumatoid arthritis: findings of a phase IIIb, multinational, prospective, randomized study. Arthritis Rheum. 65, 28–38 (2013).

  32. 32.

    et al. Comparison of tocilizumab monotherapy versus methotrexate monotherapy in patients with moderate to severe rheumatoid arthritis: the AMBITION study. Ann. Rheum. Dis. 69, 88–96 (2010).

  33. 33.

    et al. Peripheral and site-specific CD4+CD28null T cells from rheumatoid arthritis patients show distinct characteristics. Scand. J. Immunol. 79, 149–155 (2014).

  34. 34.

    et al. Citrulline-specific Th1 cells are increased in rheumatoid arthritis and their frequency is influenced by disease duration and therapy. Arthritis Rheumatol. 66, 1712–1722 (2014).

  35. 35.

    et al. Cell-type-restricted anti-cytokine therapy: TNF inhibition from one pathogenic source. Proc. Natl. Acad. Sci. USA 113, 3006–3011 (2016).

  36. 36.

    & Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

  37. 37.

    The BioBank Japan Project. Clin. Adv. Hematol. Oncol. 5, 696–697 (2007).

  38. 38.

    et al. A regulatory variant in CCR6 is associated with rheumatoid arthritis susceptibility. Nat. Genet. 42, 515–519 (2010).

  39. 39.

    et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 24, 14–24 (2014).

  40. 40.

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

Download references


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


  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


  1. Search for Kazuyoshi Ishigaki in:

  2. Search for Yuta Kochi in:

  3. Search for Akari Suzuki in:

  4. Search for Yumi Tsuchida in:

  5. Search for Haruka Tsuchiya in:

  6. Search for Shuji Sumitomo in:

  7. Search for Kensuke Yamaguchi in:

  8. Search for Yasuo Nagafuchi in:

  9. Search for Shinichiro Nakachi in:

  10. Search for Rika Kato in:

  11. Search for Keiichi Sakurai in:

  12. Search for Hirofumi Shoda in:

  13. Search for Katsunori Ikari in:

  14. Search for Atsuo Taniguchi in:

  15. Search for Hisashi Yamanaka in:

  16. Search for Fuyuki Miya in:

  17. Search for Tatsuhiko Tsunoda in:

  18. Search for Yukinori Okada in:

  19. Search for Yukihide Momozawa in:

  20. Search for Yoichiro Kamatani in:

  21. Search for Ryo Yamada in:

  22. Search for Michiaki Kubo in:

  23. Search for Keishi Fujio in:

  24. Search for Kazuhiko Yamamoto in:


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.

About this article

Publication history






Further reading