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Polygenic burdens on cell-specific pathways underlie the risk of rheumatoid arthritis

Nature Genetics volume 49, pages 1120–1125 (2017)Cite this article

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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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Figure 1: Strategy to identify the candidate causal pathways of immune-related complex diseases.
Figure 2: Upregulation of CCR2 in monocytes is a potential cause of Behçet's disease.
Figure 3: Characterization of complex traits by enrichment of immune-cell eQTL variants within their GWAS variants.
Figure 4: Dysregulation of CLECL1 isoform balance in B cells is a potential cause of multiple sclerosis.
Figure 5: Epigenomic data improved the performance of gene expression prediction.
Figure 6: Candidate causal genes and cytokine pathways in RA.

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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.

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Authors and 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.

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Correspondence to Yuta Kochi.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–17 and Supplementary Tables 1 and 14 (PDF 5848 kb)

Supplementary Table 2

Enrichment of cell-specific eQTL variants within transcription factor binding sites. (XLSX 125 kb)

Supplementary Table 3

List of candidate causal genes identified by combining GWAS catalog and eQTL data of each cell type. (XLSX 21945 kb)

Supplementary Table 4

List of candidate causal genes identified by combining GWAS catalog and exon-level eQTL data of each cell type. (XLSX 3917 kb)

Supplementary Table 5

List of candidate causal genes identified by combining GWAS catalog and TSS-conditioned eQTL data of each cell type. (XLSX 1376 kb)

Supplementary Table 6

Bayesian test for colocalisation between GWAS variants of RA and eQTL variants of each cell type. (XLSX 14 kb)

Supplementary Table 7

eQTL variants and their effect sizes used to predict gene expression of CD4+ T cells. (XLSX 3086 kb)

Supplementary Table 8

eQTL variants and their effect sizes used to predict gene expression of CD8+ T cells. (XLSX 3034 kb)

Supplementary Table 9

eQTL variants and their effect sizes used to predict gene expression of B cells. (XLSX 4168 kb)

Supplementary Table 10

eQTL variants and their effect sizes used to predict gene expression of NK cells. (XLSX 3605 kb)

Supplementary Table 11

eQTL variants and their effect sizes used to predict gene expression of monocytes. (XLSX 5041 kb)

Supplementary Table 12

eQTL variants and their effect sizes used to predict gene expression of PB. (XLSX 4484 kb)

Supplementary Table 13

Genes with Bonferroni significance in the case-control analysis using predicted gene expression. (XLSX 13 kb)

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Ishigaki, K., Kochi, Y., Suzuki, A. et al. Polygenic burdens on cell-specific pathways underlie the risk of rheumatoid arthritis. Nat Genet 49, 1120–1125 (2017). https://doi.org/10.1038/ng.3885

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