Systematic mapping and prediction of gene-enhancer connections

项目总结 本附录中描述的培训计划旨在通过以下方式为NIH的科学工作人员带来多样性 支持未被充分代表的少数民族(URM)本科生进行独立研究 以确定人类人口中疾病风险的新机制。 现代生物学中的一个根本挑战是确定控制 基因表达，这可能有助于解释相关的数千个非编码遗传变异 通过全基因组关联研究与人类疾病的关系。解读Res和Res的功能 非编码遗传变异一直是具有挑战性的，因为我们缺乏系统地扰乱 Res在基因组中的原始位置。为了应对这一挑战，我们最近开发了一种高吞吐量 一种绘制数千个RE在其天然基因组环境中的功能并测量其数量的方法 对基因表达的影响(CRISPRi平铺)。我们还开发了一种新的分析方法来建模和预测 基于染色质状态图和3D折叠的基因-RE连接。总而言之，这些进步推动了 一种策略，允许对控制任何给定细胞类型中任何给定基因的所有RE进行系统定位。 父提案的目的是应用这些工具来表征基因-RE连接的网络架构 跨越数百种细胞类型，并编辑由细胞模型中的模型识别的单核苷酸变体以 描述它们对基因表达的影响。这些目标将提供对机制和 基因-RE连接性的架构，生成用于在任何细胞类型中绘制基因-RE连接性图谱的工具，以及 揭示常见疾病的潜在机制。 这里介绍的项目补充将以这个项目为平台，提供个性化的培训和 一个URM学生的导师计划，该计划包括一个独立的项目，在计算和 实验基因组学；关于科学、写作和职业发展的一对一指导；以及协作 与斯坦福大学遗传学系Engreitz实验室更广泛的科学团队的互动 大学。这一培训计划将把一名URM学生推向基因组学研究的前沿 确定人类人口中疾病风险的新机制。

PROJECT SUMMARY The training program described in this supplement aims to bring diversity to NIH’s scientific workforce by supporting an underrepresented minority (URM) undergraduate student in an independent research experience to identify new mechanisms of disease risk in the human population. A fundamental challenge in modern biology is to identify the noncoding regulatory elements (REs) that control gene expression, which could inform the interpretation of the thousands of noncoding genetic variants associated with human diseases through genome-wide association studies (GWAS). Interpreting the functions of REs and noncoding genetic variants has been challenging because we have lacked the ability to systematically perturb REs in their native locations in the genome. To address this challenge, we recently developed a high-throughput method to map the functions of thousands of REs in their native genomic contexts and measure their quantitative effects on gene expression (CRISPRi tiling). We also developed a novel analytical approach to model and predict gene-RE connections based on maps of chromatin state and 3D folding. Together, these advances motivate a strategy to allow systematic mapping of all of the REs that control any given gene in any given cell type. The parent proposal aims to apply these tools to characterize the network architecture of gene-RE connections across hundreds of cell types, and edit single-nucleotide variants identified by the model in cellular models to characterize their effects on gene expression. These aims will provide insights into the mechanisms and architecture of gene-RE connectivity, generate tools for mapping gene-RE connectivity in any cell type, and reveal mechanisms underlying common diseases. The project supplement described here will use this project as a platform to provide a personalized training and mentorship program for a URM student involving an independent project at the interface of computational and experimental genomics; one-on-one mentorship on science, writing, and career development; and collaborative interactions with a broader scientific team in the Engreitz Laboratory in the Department of Genetics at Stanford University. Together, this training program will catapult a URM student to the forefront of genomics research to identify new mechanisms of disease risk in the human population.