Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo.
Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo.
复制标题
DOI：
10.1038/ng.3432
复制
发表时间：
2015-12
期刊：
影响因子：
30.8
通讯作者：
Stamatoyannopoulos JA
中科院分区：
文献类型：
--
作者：
Maurano MT;Haugen E;Sandstrom R;Vierstra J;Shafer A;Kaul R;Stamatoyannopoulos JA
人类调控区的功能非常依赖于其局部基因组环境和细胞环境，这使得对调控DNA内常见疾病和性状相关变异的实验分析变得复杂。我们利用包含166个个体和114种细胞类型的等位基因解析的基因组DNA酶I足迹数据，鉴定出超过60，000种直接影响体内转录因子占用率和调控DNA可及性的常见变异。这些数据的空前规模使得能够系统地分析序列变异对体内转录因子占有率的影响。我们利用这种分析来开发影响不同转录因子识别位点的变异的准确模型，并应用这些模型来区分近50万种可能影响人类基因组中转录因子占用率的常见调控变体。该方法和结果为完整人类基因组中非编码变异的分析和解释以及疾病相关变异的系统级调查提供了新的基础。
The function of human regulatory regions depends exquisitely on their local genomic environment and cellular context, complicating experimental analysis of the expanding pool of common disease- and trait-associated variants that localize within regulatory DNA. We leverage allelically resolved genomic DNaseI footprinting data encompassing 166 individuals and 114 cell types to identify >60,000 common variants that directly impact transcription factor occupancy and regulatory DNA accessibility in vivo. The unprecedented scale of these data enable systematic analysis of the impact of sequence variation on transcription factor occupancy in vivo. We leverage this analysis to develop accurate models of variation affecting the recognition sites for diverse transcription factors, and apply these models to discriminate nearly 500,000 common regulatory variants likely to affect transcription factor occupancy across the human genome. The approach and results provide a novel foundation for analysis and interpretation of noncoding variation in complete human genomes, and for systems-level investigation of disease-associated variants.