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

与人类特征相关的调控变异的多尺度功能剖析和建模

• 批准号: 10585180
• 负责人: Steven K. Reilly
• 金额: $ 74.64万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-16 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10585180
• 关键词: Address; Alleles; Architecture; Binding; Biological Assay; CCRL2 gene; CRISPR screen; CRISPR/Cas technology; Catalogs; Cells; ChIP-seq; Chromosome Mapping; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Complex; Data; Deposition; Detection; Development; Disease; Dissection; Epigenetic Process; Etiology; Gene Expression; Gene Targeting; Genes; Genetic; Genetic Transcription; Genetic Variation; Genome; Genomic Segment; Genomics; Goals; Health; Human; Human Genetics; Human Genome; Individual; Investigation; Knowledge; Link; Lipids; Location; Logic; Machine Learning; Maps; Mediating; Metabolic; Metabolic Diseases; Modeling; Molecular; Mutagenesis; Mutagens; Mutation; Nucleic Acid Regulatory Sequences; Nucleotides; Outcome; Phenotype; Population; Regulator Genes; Regulatory Element; Structure; System; Techniques; Training; Transcript; Transcriptional Regulation; Translating; Untranslated RNA; Variant; causal variant; computer framework; disorder risk; experimental study; functional genomics; gene interaction; genetic variant; genome wide association study; genomic locus; improved; insight; machine learning model; machine learning prediction; network architecture; novel; prediction algorithm; predictive modeling; tool; trait; transcription factor; transcriptome

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.

到目前为止，我们识别人类基因序列变异的能力远远超过了这一领域的能力 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特征相关的基因座，但在极少数情况下，我们是否知道 确切的原因突变，也不是其功能背后的分子机制。这种限制在很大程度上是由于 这种变异的很大一部分驻留在顺式调控区域(CRE)，在那里我们无法识别变体 监管影响或目标基因(S)是一个主要障碍。更好地理解这一监管语法- Cres中的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， 但这需要大规模扩大具有良好特征的监管突变。 为了实现这一目标，我们将采取多管齐下的方法，建立一个大规模的、监管的变体 功能目录。我们将专注于Cres基因精细定位的，可能是来自全球的因果变异 针对各种代谢特征和疾病的人群(目标1)。我们将首先确定Cre与基因的相互作用 使用高度敏感和可扩展的内生CRISPR方法。这项大规模的测绘工作将 有助于我们理解调控语法中的Cre基因靶向逻辑。我们将使用此数据绘制地图 代谢复合体特征的转录结构。然后我们提议审问序列 成百上千个性状相关CRE在其内源位置的调控语法决定因素 基因组(目标2)。我们将首先开发一种内源饱和突变系统，以产生数百个 在这些Cre中有成千上万的核苷酸变化。然后，我们将分析这些机构的监管架构 利用多重扩增芯片测序来识别表观遗传变化，并使用HCR-FlowFISH来识别表观遗传变化 检测转录变化。除了确定各种代谢性疾病的因果变异外，这项研究 提案将产生一个曲目 300,000多个具有功能特征的监管变体。这个变种 IMPACT目录将作为一个理想的训练集，使用我们功能强大的机器来模拟规则语法 学习方法。我们将把内源饱和突变数据合并到我们的变异效应中 预测模型(VEP)。重要的是，这样的模型将在全球人口中找到实用之处 解释人类基因组的通用调控代码，从而能够解释特定于人群的 变种。然后，我们将把这些VEP部署到未被研究的变异和未被研究的种群中。 总体而言，本提案旨在生成多个级别的功能特性目录： 首先为数以千计的因果变异提供分子机制和基因靶点，然后建立 对表型相关的复杂性状的全面基因组病因学理解，最后 提供改善视觉诱发电位所需的内生数据的规模。我们的方法结合了我们团队的 横跨功能基因组学、CRISPR筛查、统计遗传学和机器学习的独特专业知识。