Function-based exploration of genetic variation at genome-scale

基于功能的基因组规模遗传变异探索

• 批准号: 10367604
• 负责人: Lars M Steinmetz
• 金额: $ 78.69万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-09 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367604
• 关键词: 3-Dimensional; ATAC-seq; Alleles; Binding; Biological Models; CRISPR/Cas technology; Cell Line; Cell model; Cells; ChIP-seq; Chromatin; Chromosome 11; Chromosomes; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Complex; Computer Models; Coupling; DNA; DNA sequencing; DNase I hypersensitive sites sequencing; Data; Data Set; Disease; Elements; Engineered Gene; Engineering; Enhancers; Future; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Genetic Variation; Genome; Genomics; Hi-C; Human; Human Chromosomes; Human Engineering; Individual; Knowledge; Link; Location; Logic; Measures; Mediating; Methods; Modeling; Molecular; Nucleic Acid Regulatory Sequences; Organism; Outcome; Performance; Phenotype; Primary Cell Cultures; Process; Proteins; Reading; Regulator Genes; Regulatory Element; Reporting; Resolution; Technology; Testing; Time; Tissues; Training; Untranslated RNA; Variant; base; base editing; causal variant; cell type; clinically relevant; cost; disease phenotype; disorder risk; effective therapy; functional genomics; gene regulatory network; genetic element; genetic variant; genome editing; genome sequencing; genome wide association study; genome-wide; genomic data; genomic tools; improved; machine learning model; novel; novel strategies; precision medicine; predictive modeling; promoter; screening; tool; trait; transcription factor; transcriptome sequencing; transcriptomics; variant of unknown significance

PROJECT SUMMARY Genome-wide association studies have discovered thousands of genetic variants associated with phenotypic traits such as disease risk. Most of the associated variation lies within non-coding regions of the genome and the causative effects of those variants remain largely unknown. The sparsity of knowledge on interactions between the coding and non-coding regulatory parts of the genome makes the prediction of variant function solely from genome sequence and location impossible. We propose to experimentally uncover the functional relevance of genetic variants at a large scale, by perturbing variants and genetic elements containing variants, and reading out the direct consequences of those perturbations on gene regulation. To this end, we propose to apply our recently developed CRISPR/Cas9 functional genomics screening technology with targeted single-cell transcriptomic readouts (targeted Perturb-seq or TAP-Seq in short) to enable systematic interrogation of non- coding regions and genetic variation therein. First, we will apply our targeted Perturb-seq to decipher the regulatory circuitry encoded on an entire human chromosome by systematically perturbing all major genetic elements (enhancers, protein-coding and lncRNA genes). This extensive data set will enable to decipher the complex regulatory networks controlling gene expression on the selected chromosome. Next, we will uncover causal regulatory variants in these regions by coupling high-throughput precision genome editing to simultaneous single-cell genomic and transcriptomic readout. Using this novel approach, we will be able to decipher the functional impact of genetic variants on gene expression and derive rules by which genetic variation perturbs gene regulatory processes. We will integrate the generated data with available functional genomics data, such as transcription factor binding (ChIP-seq), chromatin accessibility (ATAC-seq, DNAse-seq) and interactions in 3D (Hi-C), in order to train machine learning models to derive rules of the observed regulatory interactions. These models will be applied to decipher the molecular mechanisms underlying the regulatory logic, and to predict regulatory interactions and variants throughout the genome and across cell types. Selected predictions will be experimentally validated using the established perturbation technologies, to verify clinically relevant predictions and improve the performance of the predictive models. Taken together, this project will answer fundamental questions in gene regulation, uncover the mechanisms by which genetic variation impacts gene expression, and create datasets and computational models as valuable tools for interpreting results from GWAS, eQTL and clinical genomic studies.

项目概要 全基因组关联研究发现了数千种与表型相关的遗传变异 疾病风险等特征。大多数相关变异位于基因组的非编码区域内 这些变异的致病影响在很大程度上仍然未知。交互知识的稀疏性 基因组的编码和非编码调控部分之间的关​​系可以预测变异功能 仅从基因组序列和位置来看是不可能的。我们建议通过实验揭示功能 通过扰动变异和包含变异的遗传元件，大规模遗传变异的相关性， 并读出这些扰动对基因调控的直接后果。为此，我们建议 应用我们最近开发的针对单细胞的CRISPR/Cas9功能基因组筛选技术 转录组读数（简称为靶向 Perturb-seq 或 TAP-Seq），以实现对非 编码区和其中的遗传变异。首先，我们将应用我们的目标 Perturb-seq 来破译 通过系统地干扰所有主要遗传基因，在整个人类染色体上编码的调节电路 元件（增强子、蛋白质编码和 lncRNA 基因）。这个广泛的数据集将有助于破译 控制所选染色体上基因表达的复杂调控网络。接下来我们就来揭秘 通过将高通量精准基因组编辑与这些区域的因果调控变异相结合 同时读取单细胞基因组和转录组。使用这种新颖的方法，我们将能够 破译遗传变异对基因表达的功能影响，并推导出遗传变异的规则 扰乱基因调控过程。我们将把生成的数据与可用的功能基因组学整合起来 数据，例如转录因子结合 (ChIP-seq)、染色质可及性（ATAC-seq、DNAse-seq）和 3D（Hi-C）交互，以训练机器学习模型来导出观察到的监管规则 互动。这些模型将用于破译调控逻辑背后的分子机制， 并预测整个基因组和跨细胞类型的调控相互作用和变异。已选择 将使用已建立的扰动技术对预测进行实验验证，以进行临床验证 相关预测并提高预测模型的性能。综合起来，该项目将 回答基因调控的基本问题，揭示遗传变异影响的机制 基因表达，并创建数据集和计算模型作为解释结果的有价值的工具 GWAS、eQTL 和临床基因组研究。