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: 9356566
• 负责人: Alexis Battle
• 金额: $ 46.38万
• 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/9356566
• 关键词: Address; Adopted; Affect; Architecture; Behavior; Biological Assay; Biological Models; Cardiac Myocytes; Cell Differentiation process; 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; Modality; Modeling; Molecular; Neurons; Outcome; Phenotype; Process; Psychological Transfer; Quantitative Trait Loci; Regulatory Element; Resources; Sampling; Specific qualifier value; Specificity; Statistical Methods; Supporting Cell; System; Time; Tissues; Untranslated RNA; Variant; base; cell type; computerized tools; data resource; genetic variant; genome sequencing; genomic data; histone modification; human disease; human genomics; human tissue; improved; induced pluripotent stem cell; learning network; learning strategy; novel; phenotypic data; 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.

人类基因组学目前面临的主要挑战之一是剖析序列变异对基因表达的影响， 调控描述监管变化的功能后果将导致更大的 理解基因表达的进化限制，改进对全基因组的解释 测序，以及复杂疾病的遗传结构的更完整的图片。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， 对细胞类型和环境背景具有高度特异性。了解疾病相关的遗传变异 影响人体组织需要我们确定细胞类型特异性调控变异背后的机制。在 为了解决这些重要的目标，我们建议创建一个资源，我们可以直接分析 多种细胞类型中的调节表型的范围。我们建议建立一个小组的诱导多能 我们从70个个体中收集了多能干细胞（iPSC），并从iPSC和分化细胞中收集了基因组数据。这 将通过制定一种新的统计方法来补充经验性的努力， 不同的检测方法、细胞类型、时间点和个体;以稳健地识别调控网络和遗传 在每种情况下影响基因调控的变异。这些贡献将共同为以下方面提供基础： 正在进行的研究基因调控和序列变异在多种疾病相关的细胞类型，使用 可更新的模型系统和新的方法，这些数据的强大分析。在目标1中，我们建议 开发70个iPSC细胞系的资源，我们将在那里收集广泛的分子调控表型， 在分化为三种细胞类型（心肌细胞、神经元和肝细胞）的整个过程中的多个时间点。 在整个分化过程中测量每个样品中的基因表达、染色质可及性和甲基化， 我们将提供一个详细的图片级联调节影响活跃在每种细胞类型和过程中， 发展在目标2中，我们提出了一个新的统计框架，用于推断普遍和细胞- 类型特异性调节因子，来自跨越细胞类型、个体、表型和 时间点。我们指定了一种基于贝叶斯分层迁移学习的机器学习方法， 提供了显著增强的能力，以检测共享效应，同时明确识别特定于上下文的 监管变化。这种方法将被用来推断监管网络，确定关键的监管 序列元件，并在每个表型和细胞类型中定位QTL。在目标3中，我们将利用经验数据 和新的方法来推断调控关系和机制的遗传变异相关 与原发组织中的基因表达和疾病有关。为此，我们将仔细整合 外部关联研究。所有样品、细胞系、数据、计算工具和分析结果将在 免费提供给社区。我们希望我们的项目将大大促进对基因的理解， 调节，不同细胞类型中遗传变异的后果，以及疾病的遗传基础。