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人类心脏调控元件、其同源转录因子和调控 DNA 序列变异。

Human cardiac -regulatory elements, their cognate transcription factors, and regulatory DNA sequence variants.

机构信息

McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.

Department of Pediatrics and Center for Genomic and Computational Biology, Duke University Medical Center, Durham, North Carolina 27708, USA.

出版信息

Genome Res. 2018 Oct;28(10):1577-1588. doi: 10.1101/gr.234633.118. Epub 2018 Aug 23.

DOI:10.1101/gr.234633.118
PMID:30139769
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6169896/
Abstract

-regulatory elements (CRE), short DNA sequences through which transcription factors (TFs) exert regulatory control on gene expression, are postulated to be the major sites of causal sequence variation underlying the genetics of complex traits and diseases. We present integrative analyses, combining high-throughput genomic and epigenomic data with sequence-based computations, to identify the causal transcriptional components in a given tissue. We use data on adult human hearts to demonstrate that (1) sequence-based predictions detect numerous, active, tissue-specific CREs missed by experimental observations, (2) learned sequence features identify the cognate TFs, (3) CRE variants are specifically associated with cardiac gene expression, and (4) a significant fraction of the heritability of exemplar cardiac traits (QT interval, blood pressure, pulse rate) is attributable to these variants. This general systems approach can thus identify candidate causal variants and the components of gene regulatory networks (GRN) to enable understanding of the mechanisms of complex disorders on a tissue- or cell-type basis.

摘要

调控元件(CRE)是短的 DNA 序列,转录因子(TFs)通过它们对基因表达发挥调控作用,被认为是复杂性状和疾病遗传的因果序列变异的主要位点。我们提出了综合分析,将高通量基因组和表观基因组数据与基于序列的计算相结合,以识别给定组织中的因果转录成分。我们使用成年人心脏的数据来证明:(1)基于序列的预测检测到许多活跃的、组织特异性的 CRE,这些 CRE 被实验观察所遗漏;(2)学习到的序列特征识别出同源的 TF;(3)CRE 变体与心脏基因表达特异性相关;(4)典型心脏特征(QT 间期、血压、脉搏率)的遗传率的很大一部分归因于这些变体。因此,这种通用的系统方法可以识别候选因果变体和基因调控网络(GRN)的组成部分,从而能够在组织或细胞类型的基础上理解复杂疾病的机制。

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