Going from Genetic Associations to Identification of Causative Genes

从遗传关联到致病基因的识别

• 批准号: 10555812
• 负责人: Allan I Pack
• 金额: $ 66.63万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10555812
• 关键词: 3-Dimensional; ATAC-seq; Adipocytes; Affect; Anatomy; Animal Model; Animals; Astrocytes; Behavior; Biological; Biological Assay; Biological Models; CRISPR/Cas technology; Candidate Disease Gene; Cells; Chromatin; Chromatin Loop; Chromosome Mapping; Clinical; Communities; Complement; Complex; Data; Disease; Drosophila genus; Drowsiness; Enhancers; Excessive Daytime Sleepiness; Fatty acid glycerol esters; Fishes; Gene Expression; Gene Targeting; Genes; Genetic; Genome; Genomics; Genotype; Goals; Human; Image; Induced pluripotent stem cell derived neurons; Investments; Knock-out; Knowledge; Link; Magnetic Resonance Imaging; Maps; Medicine; Mesenchymal Stem Cells; Methods; Modeling; Mus; Neuroglia; Neurons; Obesity; Obstructive Sleep Apnea; Osteoblasts; Pathway interactions; Patients; Phenotype; Process; Quantitative Trait Loci; RNA Interference; RNA interference screen; Research; Resolution; Resources; Risk; Risk Factors; Sampling; Severities; Shapes; Signal Transduction; Site; Sleep; Sleep Apnea Syndromes; Sleep Fragmentations; Sleep disturbances; Sleeplessness; Testing; Tongue; Transposase; Validation; Variant; Zebrafish; candidate identification; causal variant; cell type; clinical translation; craniofacial; disorder risk; diverse data; fly; follow-up; gene function; genetic association; genetic variant; genome wide association study; genome-wide; genomic locus; in silico; knock-down; knockout gene; multidimensional data; novel; precision medicine; programs; promoter; sleep behavior; trait; transcriptome sequencing

ABSTRACT Many studies, including analyses in this overall Program of research, have conducted genome-wide association studies (GWAS) to identify genes and loci associated with complex disease. While a number of genetic association signals have been uncovered, the challenge of identifying causative genes at these genetic loci remains. As such, the goal of this Project is to move from GWAS association to identification of causal effector genes relevant to obstructive sleep apnea (OSA) and excessive sleepiness, two key traits studied in this Program. To fill this critical gap in evidence this Project combines state-of-the-art approaches in cell-based and animal models. Analyses will begin by interrogating genetic loci from recent GWAS on OSA and sleepiness, and be extended to evaluate loci from ongoing analyses, including those in Projects 01 and 03. In Aim 1, we will conduct in silico physical `variant to gene mapping' based on our established Assay for Transposase Accessible Chromatin sequencing (ATAC-seq) plus genome-wide promotor-focused Capture C data on relevant cell types - osteoblasts and adipocytes (for anatomy; relevant to Project 01) and neurons and primary astrocytes (for sleepiness; relevant to Project 03). These 3D Genomics analyses will identify the most likely causal genes and variants by determining which candidates at implicated loci directly interact with regions of open chromatin. After identifying likely causal genes and variants, follow-up analyses in animal models will be performed to understand if candidate effector genes act by affecting OSA-related anatomy in mice (Aim 2) and sleep behavior reflective of sleepiness and disturbed sleep in Drosophila and zebrafish (Aim 3). Specifically, Aim 2 will utilize the novel multivariate genotype-phenotype mapping pipeline we developed to identify causal genes affecting OSA risk through effects on craniofacial shape and/or tongue fat. In applying this method, this Aim leverages both existing and newly generated data in a large sample of Diversity Outbred mice with genetic data and anatomical traits quantified via imaging. This cutting-edge approach facilitates determination of the effects of multiple genes on multivariate phenotypes using high-dimensional data to compare directions, not just magnitudes, of associations. For sleepiness, Aim 3 will utilize Drosophila RNAi lines to study the impact of knocking-down candidate genes on sleep-related phenotypes (including sleep amounts, sleep fragmentation, and sleep drive). Then, genes shown to affect sleep in Drosophila will be validated in a vertebrate model by utilizing CRISPR-Cas9 methods to knock-out these same genes in zebrafish and studying the effects on similar sleep phenotypes. Taken together, results in this Project and the other projects in this Program will provide essential knowledge about effector genes for OSA and for sleepiness in OSA in humans. This knowledge is crucial for understanding the biological and clinical impact of GWAS associations, and will facilitate more efficient clinical translation and incorporation of genetic evidence into precision medicine approaches to OSA, as well as provide an important resource for the broader scientific community and proof of principle for the field of applied genomics in OSA.

抽象的 许多研究，包括整个研究计划中的分析，都进行了全基因组研究 关联研究（GWAS）来识别与复杂疾病相关的基因和基因座。虽然一些 遗传关联信号已经被发现，识别这些遗传信号的致病基因是一项挑战。 基因座仍然存在。因此，该项目的目标是从 GWAS 关联转向因果关系识别 与阻塞性睡眠呼吸暂停（OSA）和过度嗜睡相关的效应基因，这是本研究中研究的两个关键特征 程序。为了填补这一关键证据空白，该项目结合了基于细胞和 动物模型。分析将首先询问最近 GWAS 中关于 OSA 和嗜睡的基因位点，以及 扩展到评估正在进行的分析中的位点，包括项目 01 和 03 中的分析。在目标 1 中，我们将 基于我们已建立的转座酶可及性检测，进行计算机物理“基因图谱变体” 染色质测序 (ATAC-seq) 加上以全基因组启动子为中心的捕获相关细胞类型的 C 数据 - 成骨细胞和脂肪细胞（用于解剖学；与项目 01 相关）以及神经元和初级星形胶质细胞（用于 困倦；与项目 03 相关）。这些 3D 基因组学分析将识别最可能的致病基因并 通过确定相关位点上的哪些候选者直接与开放染色质区域相互作用来识别变体。后 确定可能的致病基因和变异，将在动物模型中进行后续分析以了解 候选效应基因是否通过影响小鼠 OSA 相关解剖结构（目标 2）和反映睡眠行为来发挥作用 果蝇和斑马鱼的嗜睡和睡眠障碍（目标 3）。具体来说，《目标 2》将利用小说 我们开发的多变量基因型-表型作图流程用于识别影响 OSA 风险的致病基因 通过对颅面形状和/或舌头脂肪的影响。在应用该方法时，该目标利用了现有的 以及在具有遗传数据和解剖特征的多样性远交小鼠大样本中新生成的数据 通过成像进行量化。这种尖端方法有助于确定多个基因对 使用高维数据来比较关联的方向（而不仅仅是幅度）的多变量表型。 对于困倦，Aim 3 将利用果蝇 RNAi 系来研究敲除候选基因的影响 与睡眠相关的表型（包括睡眠量、睡眠碎片和睡眠动力）。那么，基因 研究人员将利用 CRISPR-Cas9 方法在脊椎动物模型中验证对果蝇睡眠的影响 在斑马鱼中敲除这些相同的基因并研究对相似睡眠表型的影响。综合起来， 该项目和该计划中其他项目的结果将提供有关效应基因的基本知识 用于 OSA 和人类 OSA 嗜睡。这些知识对于理解生物学和 GWAS 协会的临床影响，并将促进更有效的临床转化和整合 为 OSA 的精准医学方法提供遗传证据，并为 更广泛的科学界和 OSA 应用基因组学领域的原理证明。