Large-scale efforts like the ENCODE Project have made tremendous progress in cataloging the genomic binding patterns of DNA-associated proteins (DAPs), such as transcription factors (TFs). However, most chromatin immunoprecipitation-sequencing (ChIP-seq) analyses have focused on a few immortalized cell lines whose activities and physiology differ in important ways from endogenous cells and tissues. Consequently, binding data from primary human tissue are essential to improving our understanding of in vivo gene regulation. Here, we identify and analyze more than 440,000 binding sites using ChIP-seq data for 20 DAPs in two human liver tissue samples. We integrated binding data with transcriptome and phased WGS data to investigate allelic DAP interactions and the impact of heterozygous sequence variation on the expression of neighboring genes. Our tissue-based data set exhibits binding patterns more consistent with liver biology than cell lines, and we describe uses of these data to better prioritize impactful noncoding variation. Collectively, our rich data set offers novel insights into genome function in human liver tissue and provides a valuable resource for assessing disease-related disruptions.

诸如ENCODE项目之类的大规模努力在分类DNA相关蛋白（DAP），例如转录因子（TFs）的基因组结合模式方面取得了巨大进展。但是，大多数染色质免疫沉淀测序（ChIP-seq）分析都集中在一些永生化的细胞系上，这些细胞系的活性和生理学与内源性细胞和组织有重要的不同。因此，来自原始人类组织的结合数据对于增进我们对体内基因调控的理解至关重要。在这里，我们使用ChIP-seq数据识别和分析了两个人类肝脏组织样品中20个DAP的超过440,000个结合位点。我们整合了转录组和阶段性WGS数据的结合数据，以研究等位基因DAP相互作用以及杂合序列变异对邻近基因表达的影响。我们基于组织的数据集展示的结合模式比肝细胞系更符合肝脏生物学，并且我们描述了使用这些数据更好地确定有影响力的非编码变异的优先级。总而言之，我们丰富的数据集为人类肝脏组织中的基因组功能提供了新颖的见解，并为评估疾病相关的破坏提供了宝贵的资源。