Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
Nat Genet. 2017 Jul;49(7):1120-1125. doi: 10.1038/ng.3885. Epub 2017 May 29.
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.
最近的证据表明，相当一部分复杂疾病风险等位基因以细胞特异性的方式改变基因表达。为了鉴定免疫相关复杂疾病的候选因果基因和生物学途径，我们对 105 名健康日本志愿者的五个免疫细胞亚群（CD4 T 细胞、CD8 T 细胞、B 细胞、自然杀伤 (NK) 细胞和单核细胞）和未分馏外周血进行了表达数量性状基因座 (eQTL) 分析。我们开发了一个包含三个步骤的分析流程，包括 (i) 使用我们的 eQTL 数据库和公共表观基因组数据预测个体基因表达，(ii) 基因水平关联分析，以及 (iii) 通过整合 eQTL 效应的方向预测细胞特异性途径活性。通过将该方法应用于类风湿关节炎数据集，我们鉴定了候选因果基因和细胞因子途径（CD4 T 细胞中肿瘤坏死因子 (TNF) 的上调）。我们的方法是一种有效的方法，可以描述复杂疾病的多基因贡献和潜在的生物学机制。
