eQTL mapping in iPSC-derived differentiated cardiomyocytes

iPSC 衍生的分化心肌细胞中的 eQTL 定位

• 批准号: 9510702
• 负责人: Yoav Gilad
• 金额: $ 75.05万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-15 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9510702
• 关键词: Affect; B-Lymphocytes; Blood Cell Count; Blood Pressure; Cardiac Myocytes; Cardiovascular Diseases; Cardiovascular system; Cells; Complex; Data; Disease; Environment; Evolution; Excision; Fibroblasts; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Diseases; Genetic Variation; Genomics; Genotype-Tissue Expression Project; Goals; Grain; Heart Atrium; Heritability; Human; Hypoxia; Individual; Individual Differences; Intuition; Knowledge; Left; Left Ventricular Mass; Light; Maps; Measurement; Methods; Modification; Molecular; Mutation; Noise; Oxidative Stress; Penetrance; Pharmaceutical Preparations; Phase; Phenotype; Physiological Processes; Pilot Projects; Population; Progress Reports; Property; Protocols documentation; Quantitative Trait Loci; Regulation; Regulator Genes; Research Design; Resolution; Role; Running; Sampling; Time; Triglycerides; Uncertainty; Untranslated RNA; Variant; base; biological systems; cardiovascular disorder risk; carotid intima-media thickness; cell type; clinically relevant; design; disorder risk; droplet sequencing; fitness; functional genomics; genome wide association study; genomic data; hutterite; induced pluripotent stem cell; inter-individual variation; interest; lymphoblastoid cell line; novel; phenotypic data; pluripotency; response; risk variant; single cell technology; single-cell RNA sequencing; trait

We propose to study gene regulatory noise in differentiated cardiomyocytes using single cell technology and use these functional data to identidy novel CVD risk loci. Genome-wide association studies (GWAS) have identified many variants associated with cardiovascular- related diseases, some of which are novel. However, similar to studies of other common diseases, these identified risk-associated variants fail to explain a significant portion of the genetic heritability of cardiovascular disease (CVD). Moreover, many associated variants are non-coding without obvious function, though putatively, these are involved in gene regulation. By combining results of GWAS with functional genomic data (for example, eQTL mapping), one can identify variants that influence molecular functions and are also associated with disease risk. Individual cells are expected to tolerate uncertainties in the form of both external and internal perturbations arising from variable environments or mutations. This is especially critical in the context of cell fate transitions during differentiation. It has long been recognized that robustness is an inherent property of all biological systems and is strongly favored by evolution. Depending on their different roles, the regulation of subsets of genes is required to be particularly robust in order to maintain the phenotype or the identity of a cell. Many dynamic physiological processes must also be robust, and as a result, loss or grain of robustness is associated with certain clinically relevant phenotypes and complex genetic disease. In particular, inter-individual variation in penetrance of a mutation associated with a disease, or the variability in response to drug, can also be explained at times by different degrees of robustness. Despite the importance of robustness and the regulation of noise as mechanisms that maintain high fitness, we still have a relatively poor understanding of how robustness is achieved and how is noise being regulated at the molecular level. To take first steps towards understanding how robustness is regulated in humans, and to identify loci where mutations can affect robustness and underlie CVD risk, we will use single cell technology to collect gene expression data from differentiated cardiomyocytes. We will use a detailed time course design and high- resolution single cell gene expression data. Our approach will allow us to map variance QTLs (robustness QTLs) in addition to the more standard expression QTLs. Robustness QTLs may be of particular importance as contributors to CVD risk, a diseases in which threshold effects are predominant. Specifically, we will propose to collect single cell RNA-seq throughout cardiomyocyte differentiation of 70 Hutterite individuals (Aim 1), map eQTLs, as well as genetic loci associated with inter-individual variation in gene expression robustness (Aim 2), and Integrate eQTL and robustness QTL mapping with GWAS results to identify variants associated with CVD risk and with CVD-related phenotypes (Aim 3).

我们建议使用单细胞技术研究分化心肌细胞中的基因调控噪声， 使用这些功能数据来识别新的CVD风险位点。 全基因组关联研究（GWAS）已经确定了许多与心血管疾病相关的变异， 相关疾病，其中一些是新的。然而，与其他常见疾病的研究类似， 已确定的风险相关变异不能解释心血管疾病遗传性的重要部分， 疾病（CVD）。此外，许多相关的变体是非编码的，没有明显的功能， 当然，这些都涉及基因调控。通过将GWAS的结果与功能基因组数据相结合， (for例如，eQTL作图），人们可以鉴定影响分子功能的变体，并且还 与疾病风险有关。 预计单个电池能够承受外部和内部扰动形式的不确定性 由环境变化或突变引起的。这在细胞命运转变的背景下尤其关键 在分化过程中。长期以来，人们已经认识到，稳健性是所有生物制品的固有特性。 系统，并受到进化的强烈青睐。根据它们的不同作用， 基因需要特别稳健，以维持细胞的表型或身份。许多 动态生理过程也必须是鲁棒的，因此，鲁棒性的损失或颗粒是相关联的 具有某些临床相关的表型和复杂的遗传疾病。尤其是个体间的差异 与疾病相关的突变的不稳定性，或对药物反应的变异性，也可以是 有时用不同程度的鲁棒性来解释。尽管稳健性和监管的重要性 噪声作为保持高适应性的机制，我们仍然对如何保持高适应性的机制有一个相对较差的理解。 实现了鲁棒性以及如何在分子水平上调节噪声。 采取第一步了解如何鲁棒性是在人类调节，并确定基因座， 突变可能影响稳健性并构成CVD风险的基础，我们将使用单细胞技术收集基因， 来自分化的心肌细胞的表达数据。我们将使用详细的时间课程设计和高- 分辨率单细胞基因表达数据。我们的方法将使我们能够映射方差QTL（鲁棒性 QTL）。健壮性QTL可能特别重要 作为CVD风险的贡献者，阈值效应占主导地位的疾病。具体来说，我们将 我建议在70个哈特人的心肌细胞分化过程中收集单细胞RNA-seq（目的 1），绘制eQTL，以及与基因表达稳健性个体间变异相关的遗传位点 (Aim将eQTL和鲁棒性QTL作图与GWAS结果整合，以鉴定相关的变体。 具有CVD风险和CVD相关表型（目的3）。