Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA.
Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
Cell Syst. 2021 Apr 21;12(4):353-362.e6. doi: 10.1016/j.cels.2021.02.002. Epub 2021 Mar 8.
Systematic study of tissue-specific function of enhancers and their disease associations is a major challenge. We present an integrative machine-learning framework, FENRIR, that integrates thousands of disparate epigenetic and functional genomics datasets to infer tissue-specific functional relationships between enhancers for 140 diverse human tissues and cell types, providing a regulatory-region-centric approach to systematically identify disease-associated enhancers. We demonstrated its power to accurately prioritize enhancers associated with 25 complex diseases. In a case study on autism, FENRIR-prioritized enhancers showed a significant proband-specific de novo mutation enrichment in a large, sibling-controlled cohort, indicating pathogenic signal. We experimentally validated transcriptional regulatory activities of eight enhancers, including enhancers not previously reported with autism, and demonstrated their differential regulatory potential between proband and sibling alleles. Thus, FENRIR is an accurate and effective framework for the study of tissue-specific enhancers and their role in disease. FENRIR can be accessed at fenrir.flatironinstitute.org/.
对增强子的组织特异性功能及其与疾病的关联进行系统研究是一项重大挑战。我们提出了一种整合机器学习框架 FENRIR，它整合了数千个不同的表观遗传学和功能基因组学数据集，以推断 140 多种不同人类组织和细胞类型之间增强子之间的组织特异性功能关系，为系统地识别与疾病相关的增强子提供了一种基于调控区域的方法。我们证明了它能够准确地优先考虑与 25 种复杂疾病相关的增强子。在自闭症的案例研究中，FENRIR 优先考虑的增强子在一个大型的、兄弟姐妹对照队列中表现出显著的先证者特异性新生突变富集，表明存在致病信号。我们实验验证了八个增强子的转录调控活性，包括以前未报道与自闭症相关的增强子，并证明了它们在先证者和兄弟姐妹等位基因之间的不同调控潜力。因此，FENRIR 是研究组织特异性增强子及其在疾病中的作用的一种准确有效的框架。FENRIR 可在 fenrir.flatironinstitute.org/ 上访问。
