1] Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, Massachusetts 02139, USA. [2] The Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, Massachusetts 02142, USA. [3] Department of Genetics, Department of Computer Science, 300 Pasteur Dr., Lane Building, L301, Stanford, California 94305-5120, USA.
1] Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, Massachusetts 02139, USA. [2] The Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, Massachusetts 02142, USA.
Nature. 2015 Feb 19;518(7539):317-30. doi: 10.1038/nature14248.
The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.
参考人类基因组序列为研究遗传变异及其与人类疾病的关系奠定了基础,但表观基因组学研究缺乏类似的参考。为了满足这一需求,NIH 表观基因组学研究计划联盟生成了迄今为止最大的人类原代细胞和组织表观基因组数据集。在这里,我们描述了作为该计划一部分生成的 111 个参考人类表观基因组的综合分析结果,这些数据集的组蛋白修饰模式、DNA 可及性、DNA 甲基化和 RNA 表达情况均有呈现。我们建立了调控元件的全局图谱,定义了协调活性的调控模块及其可能的激活子和抑制剂。我们表明,与疾病和特征相关的遗传变异在组织特异性表观基因组标记中富集,揭示了多种人类特征的生物学相关细胞类型,并为解释人类疾病的分子基础提供了资源。我们的研究结果表明,表观基因组学信息对于理解基因调控、细胞分化和人类疾病具有核心作用。