(Epi)Genomics Core

(Epi)基因组学核心

基本信息

批准号：
10453775
负责人：
Marcelo A. Nobrega
金额：
$ 57.24万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-19 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10453775
关键词：
ATAC-seq Alleles Allergic Disease Asthma Biological Biological Assay Biology Candidate Disease Gene Cell Line Cells Chromatin Computing Methodologies Consensus DNA Methylation Data Development Dissection Distant Enhancers Etiology Generations Genes Genetic Genetic Transcription Genome Genomic Segment Genomic approach Genomics Goals Heritability Hi-C Human Immunologics In Vitro Individual Length Link Maps Measures Mediating Modeling Pathogenesis Phenotype Property Publishing RNA Regulator Genes Regulatory Element Reporter Research Rest Specificity Statistical Methods Stimulus Testing Tissues Transcriptional Regulation Translations Untranslated RNA Variant Work bisulfite sequencing causal variant cell type chromosome conformation capture combinatorial computer framework design disorder risk epigenomics experimental study genetic association genetic variant genome wide association study genome-wide genomic data in vitro testing in vivo in vivo Model innovation new therapeutic target novel promoter response transcription factor transcriptome sequencing whole genome

项目摘要

SUMMARY While GWAS have identified hundreds of genetic variants in at least 150 loci associated with asthma and allergic diseases (AAD), the translation of those findings into a better understanding of asthma etiology have lagged significantly. This is due, in part, to the fact that most AAD-associated variants are in noncoding sequences, often at great distance from genes. The underlying consensus is that a number of these variants impart their effects in disease risk by disrupting the regulatory properties of regulatory elements, such as enhancers and promoters. The challenges to efficiently extract hypothesis-generating information from GWAS loci include i) identifying the causal variant(s) in each GWAS locus, ii) determining the type of regulatory element in which these variants are mapped, iii) inferring the tissue-specificity of these regulatory elements, iv) defining the target genes for these regulatory elements, and v) demonstrating a phenotypic effect of these variants. This Center proposal aims to develop a computational and experimental framework to tackle all these outstanding challenges. In Project 1, an innovative statistical and computational framework will be developed to link functional annotations in AAD-associated loci to identify candidate variants, regulatory sequences and genes that are mediating the genetic association. These annotations will be generated from cells obtained in Project 2, which iteratively will also be able to test some of the predictions made from Project 1 in in vitro and in vivo models. The generation of the functional annotations necessitate the use of several state-of-the-art genomics strategies. The goal of the (Epi)Genomics Core (EGC) is to serve as the genomics data generation hub for this Center. We propose t carry over 500 whole-genome assays in multiple asthma-relevant primary cell types obtained in Project 2. In Aim 1 we will generate transcription and chromatin accessibility maps for each of these cells under baseline and stimulated conditions. We will utilize a suite of complementary chromatin accessibility assays, including ATAC-seq, KAS-sew and whole genome bisulfite sequencing, in addition to RNA-seq to generate dynamic transcription maps in each cell line in response to specific stimuli. In Aim 2 we will “wire” regulatory elements to their target genes, utilizing chromatin conformation capture. Finally, we will test the regulatory potential of thousands of candidate variants identified in Project 1 in a massively parallel reporter assay. The combination of comprehensive functional annotations in multiple cell types and states represent an ambitious departure from the traditional efforts to link variants to function in single loci to a systematic approach that interrogates the whole genome at once. We anticipate that our research strategy will generate a large number of specific hypothesis that will be pursued in similar ways to what we propose in Project 2. As such, the EGC will serve as an experimental node connecting the 2 individual projects.

概括虽然 GWAS 已在至少 150 个与哮喘和哮喘相关的位点中鉴定出数百个基因变异，过敏性疾病（AAD），将这些发现转化为对哮喘病因学的更好理解这在很大程度上是由于大多数 AAD 相关变体都是非编码的。序列，通常距离基因很远，潜在的共识是，其中许多变异。通过破坏监管要素的监管特性来影响疾病风险，例如从 GWAS 中有效提取假设生成信息的挑战。位点包括 i) 识别每个 GWAS 位点的因果变异，ii) 确定监管类型这些变体被定位的元件，iii) 推断这些调节元件的组织特异性，iv) 定义这些调控元件的靶基因，以及 v) 证明这些元件的表型效应该中心提案旨在开发一个计算和实验框架来解决所有这些问题。在项目 1 中，将开发一个创新的统计和计算框架。连接 AAD 相关基因座中的功能注释，以识别候选变体、调控序列和介导遗传关联的基因这些注释将从获得的细胞中生成。项目 2，也将能够迭代地在体外和体内测试项目 1 所做的一些预测体内模型的生成需要使用几种最先进的技术。 (Epi)Genomics Core (EGC) 的目标是作为基因组数据生成。我们建议在多个哮喘相关原发病例中进行 500 多项全基因组检测。项目 2 中获得的细胞类型。在目标 1 中，我们将为以下细胞生成转录和染色质可及性图我们将利用一套互补的基础和刺激条件下的每个细胞。染色质可及性分析，包括 ATAC-seq、KAS-sew 和全基因组亚硫酸氢盐测序除了 RNA-seq 之外，还可以在每个细胞系中生成响应特定刺激的动态转录图谱。目标 2 我们将利用染色质构象捕获将调控元件“连接”至其目标基因。我们将大规模测试项目 1 中确定的数千个候选变体的监管潜力多种细胞类型的综合功能注释的组合和平行报告分析。国家代表着对传统努力的雄心勃勃的背离，传统的努力是将单个基因座中的功能变体与基因座联系起来。我们预计我们的研究策略将采用一种一次性询问整个基因组的系统方法。产生大量具体的假设，这些假设将以与我们提出的类似的方式进行追求项目 2。因此，EGC 将作为连接两个单独项目的实验节点。