Methods for analysis of regulatory variation in cellular differentiation

细胞分化调控变异的分析方法

• 批准号: 9157021
• 负责人: Alexis Battle
• 金额: $ 40.4万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-22 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9157021
• 关键词: Address; Adopted; Affect; Architecture; Behavior; Biological Assay; Biological Models; Cardiac Myocytes; Cell Line; Cells; Chromatin; Communities; Complement; Complex; Data; Data Analyses; Development; Disease; Ectoderm; Elements; Endoderm; Epigenetic Process; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Variation; Genomics; Germ Layers; Goals; Health; Hepatocyte; Hour; Human Genetics; Human body; Individual; Knowledge; Lead; Machine Learning; Maps; Measures; Mesoderm; Methodology; Methods; Methylation; Modeling; Molecular; Neurons; Outcome; Phenotype; Process; Psychological Transfer; Quantitative Trait Loci; Regulatory Element; Resources; Sampling; Specific qualifier value; Specificity; Staging; Statistical Methods; Supporting Cell; System; Time; Tissues; Untranslated RNA; Variant; base; cell type; computerized tools; genetic variant; genome sequencing; genomic data; histone modification; human disease; human genomics; human tissue; improved; induced pluripotent stem cell; learning strategy; novel; pluripotency; programs; statistics; trait; transcriptomics; whole genome

One of the key challenges in human genomics currently is to dissect the impact of sequence variation on gene regulation. Characterizing the functional consequences of regulatory variation would lead to a greater understanding of evolutionary constraint on gene expression, improved interpretation of whole genome sequencing, and a more complete picture of the genetic architecture of complex disease. However, interpreting non-coding genetic variation remains difficult, particularly due to the complexity of gene regulation, which is highly specific to cell-type and environmental context. Understanding how disease-associated genetic variation impacts human tissues requires that we identify mechanisms behind cell-type-specific regulatory variation. In order to address these important goals, we propose to create a resource in which we can directly assay a range of regulatory phenotypes in multiple cell-types. We propose to establish a panel of induced pluripotent stem cells (iPSCs) from 70 individuals and collect genomic data from iPSCs and differentiated cells. This empirical effort will be complemented by formulating a novel statistical methodology to integrate data across different assays, cell-types, time points, and individuals; to robustly identify regulatory networks and genetic variants that affect gene regulation in each context. Together, these contributions will provide a basis for ongoing study of gene regulation and sequence variation in multiple disease-relevant cell types using a renewable model system and novel methods for robust analysis of these data. In Aim 1, we propose to develop the resource of 70 iPSC lines, where we will collect extensive molecular regulatory phenotypes at multiple time points throughout differentiation to three cell types (cardiomyocytes, neurons and hepatocytes). Measuring gene expression, chromatin accessibility, and methylation in each sample throughout differentiation, we will provide a detailed picture of the cascade of regulatory influences active in each cell type and during development. In Aim 2, we propose to develop a novel statistical framework for inferring universal and cell- type-specific regulatory factors from multi-dimensional data spanning cell-types, individuals, phenotypes, and time points. We specify a machine learning method based on Bayesian hierarchical transfer learning that provides dramatically increased power to detect shared effects while explicitly identifying context-specific regulatory changes. This approach will be adopted to infer regulatory networks, identify key regulatory sequence elements, and map QTLs in each phenotype and cell type. In Aim 3, we will utilize the empirical data and novel methods to infer regulatory relationships and mechanisms underlying genetic variants associated with gene expression in primary tissue and with disease. We will do this by performing a careful integration of external association studies. All samples, cell lines, data, computational tools, and analytical results will be made freely available to the community. We expect our project will greatly advance the understanding of gene regulation, the consequences of genetic variation in diverse cell-types, and the genetic basis of disease.

目前人类基因组学面临的主要挑战之一是剖析序列变异对基因的影响 监管。表征监管变异的功能后果将导致更大的 了解进化对基因表达的限制，改进对整个基因组的解释 测序，以及复杂疾病的遗传结构的更完整的图景。然而，口译 非编码遗传变异仍然很困难，特别是由于基因调控的复杂性，这是 高度特定于细胞类型和环境背景。了解与疾病相关的基因变异 对人类组织的影响要求我们确定细胞类型特定调控变异背后的机制。在……里面 为了解决这些重要目标，我们建议创建一个资源，在其中我们可以直接分析 多种细胞类型的调节表型范围。我们建议建立一个诱导多能小组 从70个个体中提取干细胞(IPSCs)，并从IPSCs和分化细胞中收集基因组数据。这 经验工作将通过制定一种新的统计方法来补充，以将数据整合到 不同的检测、细胞类型、时间点和个体；以强有力地识别调控网络和基因 在每种情况下影响基因调控的变异。这些贡献加在一起，将为 正在进行的多种疾病相关细胞类型的基因调控和序列变异研究 可更新的模型系统和稳健分析这些数据的新方法。在目标1中，我们建议 开发70个IPSC品系的资源，在那里我们将收集广泛的分子调控表型，网址为 在向三种细胞类型(心肌细胞、神经元和肝细胞)分化的过程中有多个时间点。 在分化过程中测量每个样本中的基因表达、染色质可及性和甲基化， 我们将提供每种细胞类型和过程中活跃的调控影响的级联图 发展。在目标2中，我们建议开发一种新的统计框架，用于推断普适性和细胞- 来自跨越细胞类型、个体、表型和细胞类型的多维数据的特定类型调控因子 时间点。提出了一种基于贝叶斯递阶转移学习的机器学习方法 提供显著增强的检测共享效果的能力，同时明确识别特定于环境的 监管方面的变化。将采用这种方法来推断监管网络，确定关键监管 测序元素，并定位每个表型和细胞类型的QTL。在目标3中，我们将利用经验数据 以及推断相关基因变体潜在的调控关系和机制的新方法 与初级组织中的基因表达和疾病有关。我们将通过执行仔细集成的 外部联想研究。所有样本、细胞系、数据、计算工具和分析结果 免费提供给社区使用。我们希望我们的项目将极大地促进对基因的理解。 调控、不同细胞类型的遗传变异的后果，以及疾病的遗传基础。