Variant-to-gene mapping for brain related traits and disorders

大脑相关特征和疾病的变异到基因图谱

• 批准号: 10294500
• 负责人: Alessandra Chesi
• 金额: $ 48.75万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10294500
• 关键词: 3-Dimensional; ATAC-seq; Address; Alzheimer' s Disease; Astrocytes; Autopsy; Biological Models; Brain; Brain Mapping; Cell Line; Cells; ChIP-seq; Chromatin; Chromosome Mapping; Coculture Techniques; Complex; Coupled; Data Set; Development; Dimensions; Disease; Drug Targeting; Enhancers; Ethnic Origin; Etiology; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Genome; Genomics; Genotype-Tissue Expression Project; Goals; Heterogeneity; Human; Human Genome; Human Genome Project; Individual; Link; Linkage Disequilibrium; Mental Depression; Microglia; Neurodegenerative Disorders; Neurodevelopmental Disorder; Neurons; Organoids; Quantitative Trait Loci; Reporting; Resolution; Sampling; Science of genetics; Sex Differences; Signal Transduction; Techniques; Tissues; Translating; Untranslated RNA; Variant; base; causal variant; cell type; chromosome conformation capture; design; drug discovery; effective therapy; epigenetic marker; ethnic difference; functional genomics; genome wide association study; genome-wide; in vitro Model; indexing; individual patient; induced pluripotent stem cell; novel therapeutics; precision medicine; promoter; rare variant; sex; tool; trait; transcriptome sequencing

Project Summary Since the completion of the Human Genome Project and the HapMap Project, genetic science has identified a wealth of associations between common or rare variants and human complex traits, including diseases. GWAS has arguably been the most successful tool in this so called “post-genomic” era, yielding almost 200,000 robust associations between common SNPs and more than 5,000 human traits. However, because of linkage disequilibrium, GWAS only report genomic “signals” or “loci” tagged by index SNPs and not the underlying true causal variants. Even more crucially, GWAS cannot indicate the effector genes at these loci, which are necessary to translate these findings into development of new therapies for disease. The main challenges to identifying causal variants and effector genes are that 1) the majority of variants identified by GWAS reside in non-coding regions of the genome and are thought to regulate gene expression, often hundreds of kb away in linear distance and 2) gene expression regulation is exquisitely tissue and cell type specific. While consortia such as ENCODE and GTEx have already built high quality, publicly available genome-wide datasets for many epigenetic markers and gene expression in different tissues and cell types, some limitations exist such as the number and heterogeneity of cell and tissue types available, the use of post-mortem samples, and the limited power due to the large sample number needed for QTL studies. As an alternative approach, I propose a variant-to-gene mapping campaign based on genome-wide high-resolution, promoter-focused Capture C, a technique that detects contacts between different regions of the genome in 3D space. Coupled with other genomic techniques, i.e. ATAC-seq, ChIP-seq and RNA-seq, this approach will allow us to identify putative causal variants residing in open chromatin and with enhancer signatures, and their (transcriptionally active) effector genes (including non-coding RNAs). Importantly, this proposal will focus on brain-related traits and disorders, a field where many GWAS signals have been reported, but only a few have been definitely linked to their effector genes, including many neurodegenerative disorders still lacking effective therapies. Using a tractable in vitro model system such as human iPSC-derived neural cell types (neurons, astrocytes and microglia, including co-cultures and brain organoids), I will be able to incorporate a temporal and functional dimension to these studies, which will help us identify mechanisms of disease etiology and progression in neuro-developmental and neurodegenerative disorders. Importantly, the functional genomics studies proposed will be performed in cell lines derived from individuals of different sex and ethnicity, to explore sex and ethnicity -specific differences in gene regulation.

项目概要 自人类基因组计划和单体型图计划完成以来，遗传科学已经确定了 常见或罕见变异与人类复杂特征（包括疾病）之间存在丰富的关联。全基因组关联分析 可以说是这个所谓的“后基因组”时代最成功的工具，产生了近 200,000 常见 SNP 与 5,000 多种人类特征之间的关联。但由于联动 不平衡，GWAS 仅报告由索引 SNP 标记的基因组“信号”或“位点”，而不是潜在的真实信息 因果变异。更重要的是，GWAS 无法指示这些位点的效应基因，而这些基因位点是必需的 将这些发现转化为疾病新疗法的开发。识别的主要挑战 因果变异和效应基因是：1) GWAS 鉴定的大多数变异存在于非编码区域 基因组区域，被认为调节基因表达，线性距离通常为数百 kb 2) 基因表达调控具有严格的组织和细胞类型特异性。而 ENCODE 等联盟 和 GTEx 已经为许多表观遗传标记构建了高质量、公开可用的全基因组数据集 以及基因在不同组织和细胞类型中的表达，存在一些限制，例如数量和 可用细胞和组织类型的异质性、死后样本的使用以及由于 QTL 研究所需的大量样本。作为一种替代方法，我提出了一种变体到基因的方法 基于全基因组高分辨率、以启动子为中心的 Capture C 的绘图活动，该技术 检测 3D 空间中基因组不同区域之间的接触。与其他基因组技术相结合， 即 ATAC-seq、ChIP-seq 和 RNA-seq，这种方法将使我们能够识别存在的假定因果变异 在开放染色质中并具有增强子特征，及其（转录活性）效应基因（包括 非编码RNA）。重要的是，该提案将重点关注与大脑相关的特征和疾病，这是一个许多人都关注的领域。 GWAS 信号已被报道，但只有少数信号与其效应基因明确相关，包括 许多神经退行性疾病仍然缺乏有效的治疗方法。使用易于处理的体外模型系统，例如 作为人类 iPSC 衍生的神经细胞类型（神经元、星形胶质细胞和小胶质细胞，包括共培养物和脑细胞） 类器官），我将能够将时间和功能维度纳入这些研究中，这将帮助我们 确定神经发育和神经退行性疾病的病因和进展机制 失调。重要的是，所提议的功能基因组学研究将在源自以下细胞系中进行： 不同性别和种族的个体，探索基因调控的性别和种族特异性差异。