Multi-scale functional dissection and modeling of regulatory variation associated with human traits

我们识别人类基因序列变异的能力已经远远超过了该领域的能力， 解释这些突变。全基因组关联研究已经确定了数十万个基因组 与疾病风险和人类表型性状相关的基因座，但在少数情况下，我们知道的身份， 确切的因果突变，也不是其功能背后的分子机制。这种局限性很大程度上是由于 这种变异的很大一部分位于顺式调节区（克雷斯），在那里我们无法识别一个变异体。 调控影响或靶基因是一个主要障碍。更好地理解这种规则语法- 克雷斯中序列内容如何控制转录的复杂逻辑是基因组学的关键下一步， 但需要大量扩增表征良好的调节突变。 为了实现这一目标，我们将采用多管齐下的方法来构建大规模的监管变体 功能目录我们将专注于克雷斯窝藏遗传精细映射，可能的因果变异，从全球 人群的各种代谢性状和疾病（目标1）。我们将首先确定CRE-基因相互作用 使用高灵敏度和可扩展的内源性CRISPR方法。这项大规模的测绘工作将 告知我们对调控语法的CRE基因靶向逻辑的理解。我们将用这些数据来绘制 代谢复杂性状的转录结构。然后，我们建议询问序列 调节语法的决定因素，数百个特质相关的克雷斯在其内源性的位置， 基因组（Aim 2）。我们将首先开发内源性饱和诱变系统，以产生数百个 在这些克雷斯中有成千上万的核苷酸变化。然后，我们将分析这些基因的调控结构， 使用多重扩增子ChIP测序鉴定表观遗传变化，并使用HCR-FlowFISH 检测转录变化。除了确定各种代谢疾病的因果变异外， 建议将产生一个剧目， 300，000+功能性特征的调节变体。该变型 影响目录将作为一个理想的训练集，用我们强大的机器来模拟调节语法 学习方法。我们将把内源性饱和诱变数据纳入我们的变异效应中 预测模型（VEP）。重要的是，这些模型将在全球人群中找到实用性，因为它们将 解释人类基因组的普遍调节代码，从而能够解释特定人群的基因组。 变化量然后，我们将这些VEP部署到研究不足的变异和研究不足的人群中。 总的来说，本提案的结构是在多个级别上生成功能特性目录： 首先为数千种致病变异提供分子机制和基因靶点， 对表型相关复杂性状的全面基因组病因学理解，最后 提供改善VEP所需的内源性数据规模。我们的方法结合了我们集团的 独特的专业知识，涵盖功能基因组学，CRISPR筛选，统计遗传学和机器学习。

Our ability to identify genetic sequence variation in humans has thus far outstripped the field’s ability to interpret these mutations. Genome-wide association studies have identified hundreds of thousands of genomic loci associated with disease risk and human phenotypic traits, yet in few instances do we know the identity of the exact causal mutation, nor the molecular mechanism behind its function. Much of this limitation is due to a large portion of this variation residing in cis-regulatory regions (CREs), where our inability to identify a variants’ regulatory impacts or target gene(s) presents a major hurdle. Better understanding of this regulatory grammar - the complex logic of how sequence content in CREs controls transcription – is a crucial next step for genomics, but requires a vast expansion of well characterized regulatory mutations. To achieve this goal, we will employ a multi-pronged approach to build a large-scale, regulatory variant functional catalog. We will focus on CREs harboring genetically fine-mapped, likely causal variants from global populations for a variety of metabolic traits and disease (Aim 1). We will first identify CRE-gene interactions using highly-sensitive and scalable endogenous CRISPR approaches. This large-scale mapping effort will inform our understanding of the CRE-gene targeting logic of regulatory grammar. We will use this data to map the transcriptional architecture of metabolic complex traits. We then propose to interrogate sequence determinants of regulatory grammar for hundreds of trait-associated CREs at their endogenous location in the genome (Aim 2). We will first develop an endogenous saturation mutagenesis system to generate hundreds of thousands of nucleotide changes in these CREs. We will then assay the regulatory architecture of these changes using multiplexed amplicon ChIP-sequencing to identify epigenetic changes, and HCR-FlowFISH to detect transcriptional changes. In addition to identifying causal variants for a variety of metabolic diseases, this proposal will generate a repertoire of 300,000+ functionally characterized regulatory variants. This variant impact catalog will serve as an ideal training set to model regulatory grammar with our powerful machine learning approaches. We will incorporate endogenous saturation mutagenesis data into our variant effect prediction models (VEPs). Importantly, such models will find utility across global populations as they will explain a universal regulatory code of the human genome and thus enable interpretation of population-specific variation. We will then deploy these VEPs to understudied variation and in understudied populations. Overall, this proposal is structured to generate a functional characterization catalog at multiple levels: first providing molecular mechanisms and gene targets for thousands of causal variants, secondly building comprehensive genomic etiological understanding for phenotypically related complex traits, and lastly providing the scale of endogenous data necessary to improve VEPs. Our approach combines our group’s unique expertise spanning functional genomics, CRISPR screens, statistical genetics, and machine learning.