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Interrogating regulatory variants by multiplexed genome editing

通过多重基因组编辑询问调控变异

基本信息

批准号：
9761568
负责人：
Alon Goren
金额：
$ 23.63万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-09 至 2020-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9761568
关键词：
Address Affect Allelic Imbalance Automobile Driving Binding Biological Assay CRISPR/Cas technology Cell Fraction Cells ChIP-seq Code Computing Methodologies DNA Data Set Diabetes Mellitus Enhancers Etiology Evaluation Exhibits Frequencies Gene Expression Genes Genome Genomic DNA Genomics Genotype Goals Gold HNF4A gene Heart Diseases HepG2 Hepatocyte Human Individual Machine Learning Malignant Epithelial Cell Maps Measures Messenger RNA Methods Molecular Mutate Mutation Nucleic Acid Regulatory Sequences Oligonucleotides Open Reading Frames Phenotype Physiological Plasmids Point Mutation Population Primary carcinoma of the liver cells Proteins Quantitative Trait Loci Regulatory Element Reporter Reporter Genes Resources Schizophrenia Sorting - Cell Movement Techniques Testing Untranslated RNA Variant cell type epigenomics genetic variant genome editing genome wide association study histone modification human disease improved innovation insight interest learning strategy mRNA Expression molecular phenotype novel transcription factor

项目摘要

A major result from recent genome wide association studies (GWAS) is that the majority of genetic variants driving common human diseases lie in regulatory, rather than protein-coding, regions. Massive efforts to map epigenomic features such as localization of histone modifications (HMs) and transcription factors (TFs) have paved the way toward understanding the regulatory genome. However, dissecting the impact of an individual non-coding variant remains an unsolved challenge. A variety of computational methods have been proposed, such as quantitative trait loci (QTL) studies and machine learning techniques. However, these methods still do not provide conclusive information about causality of any specific non-coding mutation and lack gold-standard experimental results for evaluation. Several techniques are used to experimentally test the impact of individual regulatory variants. For example, massively parallel reporter assays (MPRA) synthesize thousands of oligonucleotides encoding mutated versions of putative regulatory elements placed in plasmids upstream of reporter genes. However, a major limitation is that tested sequences are outside of their endogenous chromosomal locus, and hence do not necessarily provide physiological relevance. CRISPR enables targeted editing of genomic DNA. Indeed, CRISPR is widely used, but studies of individual point mutations have been primarily on a small scale and are usually limited to a handful of variants or to a single gene. The major throughput challenge in studying a specific variant using genome editing is in tying genotype to phenotype. Introducing individual mutations exhibits low efficiency, and thus there is a need for enrichment of the genotype or phenotype of interest prior to assessing the impact of a mutation on a phenotype, such as gene expression. Current enrichment methods either disrupt the physiological context or are low throughput. Recent efforts overcame these challenges using pooled editing to analyze thousands of mutations simultaneously, but were limited to variants in protein coding regions. This proposal aims to develop a novel technique merging multiplexed genome editing of putative regulatory variants followed by chromatin immunoprecipitation sequencing (ChIP-seq) to simultaneously measure the impact of hundreds of non-coding variants on regulatory potential in their native genomic context. The key insight of the proposed approach is that mutations impacting epigenomic features can be measured both in genomic DNA and in phenotypic readouts such as ChIP-seq of TFs or HMs, avoiding the need for a selection step to connect genotypes with phenotypes. Aim 1 develops the pooled editing technique on a pilot set of previously validated regulatory variants. Aim 2 scales this approach to interrogate thousands of mutations at once. Aim 3 integrates experimental predictions with state of the art machine learning methods to evaluate and optimize computational methods for regulatory variant effect prediction.

近年来基因组广泛关联研究（GWAS）的一个主要结果是，大多数的遗传