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

项目摘要/摘要 自黑猩猩分离以来，非编码调控突变推动了人类进化，超过 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和背景的 依赖于非编码调控突变的功能应该在特定的细胞类型中定义，特别是 发育阶段。然而，这种人类疾病和疾病中非编码突变的精细分辨率图谱 进化论在文献中一直缺乏，这项提议旨在开发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。 我最近的工作开发了疾病基因组分析的创新方法，并确定了关键 推动近期人类进化的元素。鉴于非编码基因组在人类中的首要重要性 疾病和进化，我研究的长期目标是识别影响 扰乱基因调控的人类表型。建立在我们最近成功捕获致病非编码的基础上 预测前列腺癌特征的体细胞突变，在未来五年，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改变细胞类型特定表观基因组结构的等位基因效应， 揭示细胞对人类疾病或任何进化特征的贡献。为了这个目标，我的 实验室正在积极生成单细胞表观基因组数据，已经获得了大规模的基因组 不同的疾病类别，并开发了创新的深度学习模型，实现了实质性的 提高了捕获致病非编码突变的精确度。 为了证明我们的研究框架的普遍适用性，我们将研究稀有生殖系 前列腺癌的突变，早产的常见变异，以及尼安德特人引入的等位基因 现代人类基因组对影响人脑发育的调节作用。这些条件 有广泛的人口流行率，我们的初步分析清楚地证明了 我们的机器学习模型，用于识别特定细胞类型中相应的非编码突变。我们还将 构建开放访问的基因组浏览器，允许用户可视化和分析任何 特定细胞类型的突变。总体而言，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大脑发育的影响，并将提供 一种普遍适用的单细胞分辨率的基因组分析工具。不同于传统的 方法，这项拟议的研究通过引入单细胞分析将直接揭示受影响最大的细胞 疾病中的人群，从而指导针对受影响人群的治疗战略的未来发展 单元类型。

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.