Genome analysis: statistical methods and applications

项目摘要 近年来，新的数据和技术改变了我们对转录过程的理解 以及它们如何受到遗传变异的影响。GTEx项目测量了遗传变异和 在数百个个体的50个组织中的转录变异，并确定了数十万个 与基因表达相关的遗传变异(EQTL)。而技术创新现在已经 使得在单个细胞中询问全基因组转录成为可能。人类细胞图谱(HCA) Project目前正在使用这种技术来分析数百万个细胞，其雄心勃勃的目标是提供 组成人体的各种细胞类型的综合图谱。 然而，目前的分析工具在充分利用这些数据的丰富性方面的能力有限。当前 用于在50个组织中识别eQTL的分析工具在识别关联方面表现良好--这两个组织都是 特定的效果和那些在组织中广泛共享的效果-但尚未设计用于精细映射 解释这些关联信号的潜在功能变体。和方法，以总结和 单细胞间转录异质性的表征不能捕获复合层 这种异质性特征--例如，细胞可能聚集成不同的组，这取决于 基因或转录过程被考虑在内。 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 单细胞分析的简化技术，旨在捕获复杂的异质性模式， 现有方法忽略了这一点。我们将开发统计工具，以可靠地评估 显示不同细胞组之间的转录差异。我们将开发和应用各种方法来绘制精细地图 GTEx项目数据中许多eQTL背后的功能变体，充分利用了信息 并在互联网上以一种方便的形式传播结果。 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 GTEx和HCA等项目中的信息，并将它们提供给广大的生物和 可以从这一结果中受益的医学科学家。

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.