Systematic mapping and prediction of gene-enhancer connections

现代生物学中的一个根本挑战是确定控制 基因表达，这可能有助于解释数千种非编码遗传变异 通过全基因组关联研究(GWAS)与人类疾病相关。解读 RES和非编码基因变体的功能一直是具有挑战性的，因为我们缺乏 系统地扰乱RE在基因组中的原始位置。为了应对这一挑战，我最近 开发了一种高通量的方法来定位它们天然基因组中数千个RE的功能 背景和测量它们对基因表达的定量影响(CRISPRi平铺)。我还写了一本小说 基于染色质状态图和3D图的基因-RE连接建模和预测的解析方法 折叠。总而言之，这些进展推动了一项战略，以允许系统地绘制所有可持续发展的 控制任何给定细胞类型中的任何给定基因。在K99阶段，我建议：(I)将CRISPRi切片应用于地图 ~6,000个额外的基因-RE连接，以及(Ii)使用这些数据来扩展和优化模型以预测 染色质状态的基因-RE连接。我将使用人类免疫细胞作为模型系统来比较 跨细胞类型的预测。在R00阶段，我将应用这些工具来(Iii)描述网络 跨数百种细胞类型的基因-RE连接的体系结构，以及(Iv)编辑单核苷酸变体 通过该模型在细胞模型中鉴定其对基因表达的影响。加在一起，这些 AIMS将提供对基因-RE连接的机制和架构的见解，为 绘制任何细胞类型的基因-RE连接图，并揭示常见疾病的潜在机制。 斯坦福大学是我的独立实验室的理想环境，提供了所有的设施 这是拟议研究所需要的，并为协作研究提供了丰富的跨学科环境。一起， 这些目标将开启我在调控基因组学和疾病之间的独立科学生涯 遗传学。

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, I 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). I 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. In the K99 phase, I propose to: (i) apply CRISPRi tiling to map ~6,000 additional gene-RE connections, and (ii) use these data to extend and optimize a model to predict gene-RE connections from chromatin state. I will use human immune cells as a model system to compare predictions across cell types. In the R00 phase, I will apply these tools to (iii) characterize the network architecture of gene-RE connections across hundreds of cell types, and (iv) edit single-nucleotide variants identified by the model in cellular models to characterize their effects on gene expression. Together, 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. Stanford University is an ideal environment for my independent laboratory, providing all of the facilities needed for the proposed research and a rich interdisciplinary environment for collaborative studies. Together, these aims will launch my independent scientific career at the interface of regulatory genomics and disease genetics.