Gschwind Andreas R, Mualim Kristy S, Karbalayghareh Alireza, Sheth Maya U, Dey Kushal K, Jagoda Evelyn, Nurtdinov Ramil N, Xi Wang, Tan Anthony S, Jones Hank, Ma X Rosa, Yao David, Nasser Joseph, Avsec Žiga, James Benjamin T, Shamim Muhammad S, Durand Neva C, Rao Suhas S P, Mahajan Ragini, Doughty Benjamin R, Andreeva Kalina, Ulirsch Jacob C, Fan Kaili, Perez Elizabeth M, Nguyen Tri C, Kelley David R, Finucane Hilary K, Moore Jill E, Weng Zhiping, Kellis Manolis, Bassik Michael C, Price Alkes L, Beer Michael A, Guigó Roderic, Stamatoyannopoulos John A, Lieberman Aiden Erez, Greenleaf William J, Leslie Christina S, Steinmetz Lars M, Kundaje Anshul, Engreitz Jesse M
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Basic Sciences and Engineering Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford, CA, USA.
bioRxiv. 2023 Nov 13:2023.11.09.563812. doi: 10.1101/2023.11.09.563812.
Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and largescale genetic perturbations generated by the ENCODE Consortium. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 elementgene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancerpromoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.
识别转录增强子及其靶基因对于理解基因调控以及人类遗传变异对疾病的影响至关重要。在此，我们通过整合预测模型、染色质状态和三维接触的测量结果以及由ENCODE联盟产生的大规模遗传扰动，创建并评估了跨越352种细胞类型和组织的超过1300万个增强子-基因调控相互作用的资源。我们首先创建了一个系统的基准测试流程来比较预测模型，汇集了在CRISPR扰动实验中测量的10411个元件-基因对、超过30000个精细定位的表达数量性状基因座（eQTL）以及569个与可能的因果基因相关的精细定位的全基因组关联研究（GWAS）变体的数据集。利用这个框架，我们开发了一种新的预测模型ENCODE-rE2G，它在多个预测任务中达到了当前的最优性能，展示了一种涉及迭代扰动和监督机器学习以构建越来越准确的增强子调控预测模型的策略。使用ENCODE-rE2G模型，我们构建了人类基因组中增强子-基因调控相互作用的百科全书，揭示了增强子网络的全局特性，识别了具有或多或少复杂调控格局的基因在功能上的差异，并改进了将非编码变体与常见复杂疾病中的靶基因和细胞类型相联系的分析。通过对模型的解读，我们发现有证据表明，除了增强子活性和三维增强子-启动子接触之外，其他特征也指导增强子-启动子通讯，包括启动子类别和增强子-增强子协同作用。总之，这些全基因组的增强子-基因调控相互作用图谱、基准测试软件、预测模型以及关于增强子功能的见解为未来的基因调控和人类遗传学研究提供了宝贵的资源。
