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探索具有 eQTL 网络的组织中的调控。

Exploring regulation in tissues with eQTL networks.

机构信息

Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115.

Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115.

出版信息

Proc Natl Acad Sci U S A. 2017 Sep 12;114(37):E7841-E7850. doi: 10.1073/pnas.1707375114. Epub 2017 Aug 29.

Abstract

Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.

摘要

描述遗传变异对复杂表型的集体调控影响是开发基因型到表型图谱的主要挑战。我们使用表达数量性状基因座(eQTL)分析构建了二部网络,其中边缘表示遗传变异与基因表达水平之间的显著关联,并且发现网络结构提供了调控功能的信息。我们在 13 种组织中表明,这些 eQTL 网络被组织成密集的、高度模块化的社区,这些社区将经常涉及协调生物过程的基因分组。我们发现代表跨组织共享过程的社区,以及与组织特异性过程相关的社区,这些社区围绕组织特异性活性染色质区域中的变体凝聚。节点中心性也具有高度的信息量,全局和社区枢纽在调节潜力和与疾病相关的可能性方面存在差异。

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