权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
10701670
负责人：
Lars M Steinmetz
金额：
$ 70.82万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
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/10701670
关键词：
3-Dimensional ATAC-seq Alleles Binding Biological Models CRISPR/Cas technology Cell Line Cell model Cells 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 Relaxation Reporting Resolution Technology Testing Time Tissues Training Untranslated RNA Variant base editing causal variant cell type clinically relevant cost disease phenotype disorder risk effective therapy empowerment 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

项目摘要

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.

项目总结