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.

迄今为止，我们识别人类基因序列变异的能力已经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识别出数十万个基因组。 与疾病风险和人类表型特征相关的基因座，但在少数情况下我们知道基因座的身份 确切的因果突变，或其功能背后的分子机制，很大程度上是由于一个原因。 这种变异的很大一部分存在于顺式调控区域（CRE），我们无法识别变异’ 监管影响或目标基因是更好地理解这一监管语法的主要障碍。 CRE 转录中的序列内容如何控制的复杂逻辑是基因组学的关键下一步， 但需要大量扩展明确特征的调控突变。 为了实现这一目标，我们将采用多管齐下的方法来建立一个大规模的监管变体 我们将重点关注包含来自全球的基因精细映射的可能因果变异的 CRE。 人群的各种代谢特征和疾病（目标 1）我们将首先确定 CRE 基因相互作用。 使用高度敏感和可扩展的内源 CRISPR 方法，这项大规模的绘图工作将得到实现。 告知我们对调控语法的 CRE 基因靶向逻辑的理解，我们将使用这些数据来绘制地图。 然后我们建议询问代谢复杂性状的转录结构。 数百个与性状相关的 CRE 在其内源位置的调节语法的决定因素 我们将首先开发一个内源饱和诱变系统来生成数百个基因组。 然后我们将分析这些 CRE 中的数千个核苷酸变化。 使用多重扩增子 ChIP 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 除了识别各种代谢疾病的致病变异外，这还可以检测转录变化。 提案将产生一个剧目 300,000 多个具有功能特征的调控变体。 影响目录将作为理想的训练集，使用我们强大的机器对监管语法进行建模 我们将把内源饱和诱变数据纳入我们的变异效应中。 重要的是，此类模型将在全球人群中发挥作用。 解释人类基因组的通用监管代码，从而能够解释特定人群 然后，我们将把这些 VEP 部署到未充分研究的变异和未充分研究的人群中。 总体而言，该提案的结构是为了生成多个级别的功能表征目录： 首先提供数千种因果变异的分子机制和基因靶点，其次构建 对表型相关的复杂性状进行全面的基因组病因学理解，最后 提供改进 VEP 所需的内源数据规模。我们的方法结合了我们团队的方法。 涵盖功能基因组学、CRISPR 筛选、统计遗传学和机器学习的独特专业知识。