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Nat Genet. 2017 Jul;49(7):1120-1125. doi: 10.1038/ng.3885. Epub 2017 May 29.

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

Author information

1
Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
2
Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
3
Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
4
CREST, Japan Science and Technology Agency, Tokyo, Japan.
5
Institute of Rheumatology, Tokyo Women's Medical University, Tokyo, Japan.
6
Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
7
Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.
8
Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.
9
Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
10
Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
11
Statistical Genetics, Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.

Abstract

Recent evidence suggests that a substantial portion of complex disease risk alleles modify gene expression in a cell-specific manner. 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.

PMID:
28553958
DOI:
10.1038/ng.3885
[Indexed for MEDLINE]

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