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.

我们鉴定人类遗传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，但在少数情况下，我们知道 确切的因果突变，也是其功能背后的分子机制。这个限制的很大是由于 居住在顺式调节区域（CRE）中的大部分变化，我们无法识别变体 监管影响或靶基因提出了一个重大障碍。更好地了解这种法规语法 - CRES中序列含量如何控制转录的复杂逻辑 - 对于基因组学的下一步至关重要， 但是需要大量的特征性调节突变。 为了实现这一目标，我们将采用一种多管制的方法来建立大规模的监管变体 功能目录。我们将专注于具有一般绘制的一般绘制的，可能来自全球的因果变体 各种代谢特征和疾病的种群（AIM 1）。我们将首先确定Cre-Gene互动 使用高敏感和可扩展的内源性CRISPR方法。这种大规模的映射工作将 告知我们对监管语法的Cre-Gene靶向逻辑的理解。我们将使用此数据映射 代谢复杂性状的转录结构。然后，我们建议审问序列 在其内源性位置的数百个性状相关CRE的调节语法决定因素 基因组（目标2）。我们将首先开发一个内源性饱和诱变系统，以产生数百个 这些CRE的数千个核苷酸变化。然后，我们将主张这些的监管架构 使用多路复用扩增子芯片序列的变化以识别表观遗传变化，并将hcr-flowfish识别为 检测转录变化。除了确定各种代谢疾病的因果变异，这还 提案将产生 300,000多个功能表征的调节变体。这个变体 Impact目录将用作理想的培训，以使用我们强大的机器建模监管语法 学习方法。我们将将内源性饱和诱变数据纳入我们的变体效应 预测模型（VEP）。重要的是，这样的模型将在全球人口中找到效用 解释人类基因组的普遍调节守则，从而可以解释人口特定的 变化。然后，我们将部署这些VEP，以了解变化和理解人群。 总体而言，该建议的结构是生成多个级别的功能表征目录： 首先提供数千种因果变体的分子机制和基因靶标，第二建筑物 对表型相关的复杂性状的全面基因组病因理解，最后 提供改善VEP所需的内源数据的规模。我们的方法结合了我们小组的 跨越功能基因组学，CRISPR筛选，统计遗传学和机器学习的独特专业知识。