Genome analysis: statistical methods and applications

基因组分析：统计方法和应用

• 批准号: 9977226
• 负责人: MATTHEW STEPHENS
• 金额: $ 50万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-20 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9977226
• 关键词: Address; Affect; Area; Atlases; Automobile Driving; Biological; Biological Assay; Biological Process; Biology; Cells; Cellular Assay; Communities; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Development; Dimensions; Disease; Factor Analysis; Gene Expression; Genes; Genetic; Genetic Transcription; Genetic Variation; Genome; Genotype-Tissue Expression Project; Goals; Heterogeneity; Human; Human Biology; Human body; Individual; International; Internet; Joints; Learning; Linkage Disequilibrium; Maps; Measures; Medical; Methods; Modernization; Pattern; Principal Component Analysis; Process; Quantitative Trait Loci; Research Personnel; Resolution; Resources; Scientist; Signal Transduction; Source; Specificity; Statistical Methods; Structure; Techniques; Technology; Time; Tissues; Transcription Process; Variant; Work; analytical tool; causal variant; cell type; design; differential expression; genetic variant; genome analysis; genome-wide; genomic data; human disease; human tissue; improved; indexing; insight; new technology; novel; open source; public health relevance; single cell analysis; single-cell RNA sequencing; technological innovation; tool; transcriptome; treatment strategy

Project Summary In recent years new data and technologies have transformed our understanding of transcriptional processes and how they are influenced by genetic variation. The GTEx project has measured both genetic variation and transcriptional variation in 50 tissues across hundreds of individuals, and identified hundreds of thousands of genetic variants that are associated with gene expression (eQTLs). And technological innovations have now made it possible to interrogate transcription, genome-wide, in single cells. The Human Cell Atlas (HCA) project is currently using such technologies to profile millions of cells, with the ambitious goal of providing a comprehensive atlas of the diverse cell types that make up human bodies. However, current analytic tools are limited in their ability to fully exploit the richness of these data. Current analysis tools for identifying eQTLs across 50 tissues perform well for identifying associations – both tissue- specific effects and those that are broadly shared across tissues – but are not yet designed for fine-mapping the underlying functional variants that explain these association signals. And methods for summarizing and characterizing transcriptional heterogeneity among single cells are not capable of capturing the complex layered character of this heterogeneity - for example, that cells might cluster into different groups depending on which genes or transcriptional processes are considered. Here we propose to develop novel statistical methods to address these issues. We will develop dimension reduction techniques for single cell analysis, aimed at capturing the complex patterns of heterogeneity that existing methods ignore. We will develop statistical tools for reliably assessing the genes and processes that show transcriptional differences among groups of cells. And we will develop and apply methods to fine-map the functional variants underlying many of the eQTLs in the GTEx project data, fully exploiting the information in the many tissues profiled, and disseminate the results on the internet in a convenient form. The overall goal of the project is to build and apply methods and software to help fully exploit the rich information in projects like GTEx and HCA, and make them available to the broad community of biological and medical scientists who can benefit from the results.

项目摘要 近年来，新数据和技术改变了我们对转录过程的理解 以及它们如何受遗传变异的影响。 GTEX项目已经测量了遗传变异和 数百个个体的50个组织中的转录变化，并确定了数十万个 与基因表达（EQTL）相关的遗传变异。现在有技术创新 使单个细胞中的转录，全基因组的转录。人类细胞地图集（HCA） 项目目前正在使用此类技术来介绍数百万个单元，其雄心勃勃的目标是提供 构成人体的潜水细胞类型的全面地图集。 但是，当前的分析工具的充分探索这些数据丰富性的能力受到限制。当前的 用于识别50个组织中EQTL的分析工具在识别关联方面表现良好 - 两者都组织 - 特定效果以及跨组织广泛共享的效果 - 但尚未设计用于精细映射 解释这些关联信号的基本功能变体。和总结方法和 表征单细胞之间的转录异质性无法捕获复合物分层 这种异质性的特征 - 例如，细胞可能会聚集成不同的组 考虑基因或转录过程。 在这里，我们建议开发新的统计方法来解决这些问题。我们将发展维度 减少单细胞分析的技术，旨在捕获异质性的复杂模式 现有方法忽略。我们将开发统计工具，以可靠评估基因和过程 显示细胞组之间的转录差异。我们将开发并应用方法来微图 GTEX项目数据中许多EQTL的功能变体，完全利用信息 在许多组织中，并以方便的形式在互联网上传播结果。 该项目的总体目标是构建和应用方法和软件，以帮助充分利用富人 GTEX和HCA等项目中的信息，并将其提供给广泛的生物学社区和 可以从结果中受益的医学科学家。