Single-Cell Analysis of the Noncoding Genome in Human Diseases and Evolution

项目总结/摘要 自人类与黑猩猩分离以来，非编码调控突变一直推动着人类的进化， 90%的疾病相关基因座位于非编码区。因为基因调控是动态的， 非编码调控突变的功能应在特定的细胞类型中， 发育阶段然而，这种精细分辨率的人类疾病非编码突变图谱， 进化在文献中一直缺乏，本提案旨在制定一个全面的研究计划 通过将我们的基因组分析推向单细胞分辨率来缩小知识差距。 我最近的工作为疾病基因组分析开发了创新方法，并确定了关键的 推动人类进化的元素考虑到人类非编码基因组的重要性， 疾病和进化，我的研究的长期目标是确定因果非编码突变， 通过扰乱基因调控改变人类表型。基于我们最近成功捕获致病性非编码基因 预测前列腺肿瘤特征的体细胞突变，在接下来的五年里，我的研究将 将我们的基因组分析推向单细胞分辨率。通过开发一系列机器学习框架， 我们将能够直接确定等位基因对改变细胞类型特异性表观基因组结构的影响， 揭示了细胞对人类疾病或任何进化特征的贡献。为了实现这一目标，我 实验室正在积极生成单细胞表观基因组数据，已经获得了大规模基因组的访问权， 不同的疾病类别，并开发了一种创新的深度学习模型， 提高了捕获致病性非编码突变的精度。 为了证明我们的研究框架的普遍适用性，我们将调查罕见的生殖系 前列腺癌中的突变，早产中的常见变异，以及尼安德特人基因组中的基因渗入等位基因。 现代人类基因组对影响人类大脑发育的调节作用。这些条件 有广泛的人口流行，我们的初步分析清楚地表明， 我们的机器学习模型用于识别特定细胞类型中的后续非编码突变。我们还将 建立一个开放获取的基因组浏览器，使用户能够可视化和分析任何基因组的调控效果。 特定细胞类型中的突变。总的来说，拟议的计划将揭示新的疾病机制， 非编码基因组，将揭示尼安德特人对现代人类大脑发育的影响，并将提供 一种普遍适用的单细胞分辨率基因组分析工具。不同于常规 通过引入单细胞分析，这项拟议的研究将直接揭示受影响最严重的细胞 从而指导今后制定针对受影响人群的治疗战略 细胞类型。

Project Summary/Abstract Noncoding regulatory mutations had driven human evolution since the split from chimpanzees and more than 90% of disease-associated loci reside in noncoding regions. Because gene regulation is dynamic and context dependent, functions of noncoding regulatory mutations should be defined in specific cell types and at particular developmental stages. However, such a fine-resolution mapping of noncoding mutations in human diseases and evolution has been lacking in the literature, and this proposal aims to develop a comprehensive research program to close the knowledge gap by pushing our genome analysis to a single-cell resolution. My recent work has developed innovative approaches for disease genome analysis and has identified key elements driving recent human evolution. Given the prime importance of the noncoding genome in human diseases and evolution, the long-term goal of my research is to identify causal noncoding mutations that affect human phenotypes by perturbing gene regulation. Built on our recent success in capturing pathogenic noncoding somatic mutations that are predictive of prostate tumor characteristics, in the next five years, my research will push our genome analysis to a single-cell resolution. By developing a series of machine learning frameworks, we will be able to directly determine the allelic effects on altering cell-type-specific epigenomic architecture, revealing the cellular contribution to human diseases or to any evolutionary traits. Towards this goal, my laboratory is actively generating single-cell epigenome data, have obtained access to large-scale genomes for different disease categories, and have developed an innovative deep learning model achieving substantially enhanced precision for capturing pathogenic noncoding mutations. To demonstrate the general applicability of our research framework, we will investigate rare germline mutations in prostate cancer, common variants in preterm birth, and the Neanderthal introgressed alleles in the modern human genome for their regulatory effects on affecting human brain development. These conditions have wide population prevalence, and our preliminary analyses have clearly demonstrated the effectiveness of our machine learning model on identifying consequential noncoding mutations in specific cell types. We will also build an open-access genome browser which will allow users to visualize and analyze regulatory effects of any mutations in a given cell type. Overall, the proposed program will uncover new disease mechanisms from the noncoding genome, will reveal the Neanderthal impact on brain development of modern humans, and will provide a generally applicable tool for genome analysis at a single-cell resolution. Different from conventional approaches, this proposed research by introducing single-cell analysis will directly reveal the most affected cell populations in diseases, thereby guiding the future development of therapeutic strategies targeting the affected cell types.