Genetic Regulation of Gene Expression and its Impact on Phenotypes

描述（由申请人提供）： 基因型-组织表达（GTEx）计划正在投入大量资源收集数据集，这些数据集有望提供前所未有的机会来了解人类基因表达调控，其在组织中的调节及其遗传决定因素。很容易想象，各种各样的和深远的研究计划将发展解释和充分理解这一调查的结果。所有这些研究的第一步是鉴定影响一个或多个组织中一个或多个基因表达的基因座，称为表达数量性状基因座（eQTL）。该提案的重点是提供尖端的计算和统计工具，这些工具将允许以高灵敏度和低假阳性率鉴定eQTL，从而最大限度地提高GTEx样品收集和实验研究的产量。 我们将依次解决1）开发强大而敏感的测试; 2）使用统计方法控制假阳性; 3）使用独立数据集分析结果的可复制性和相关性。具体而言，我们将开发，在软件中实现免费分发，并应用于GTEx数据，测试统计量对批次效应和群体分层具有鲁棒性，但适用于多个组织中表达的协调分析，允许跨组织借用信息。我们将使用ad扩展尖端方法来建立这些测试统计量的统计显著性阈值，这些方法既要注意探索的大量假设，又要适应GTEx数据的噪声结构。最后，我们将分析三个独立的数据集，包括全基因组的基因型和表达信息以及丰富的表型数据：这一步将使我们能够评估我们的结果的可重复性，以及调查如何确定的eQTL与高阶表型。我们的团队由斯坦福大学和加州大学洛杉矶分校的研究人员组成，在代表该项目所面临的重要挑战的每个领域都有合作和卓越的记录。

DESCRIPTION (provided by applicant): Project Summary The Genotype-Tissue Expression (GTEx) program is investing considerable resources in the collection of a data set that promises to offer unprecedented opportunities to understand gene expression regulation in humans, its modulation across tissues, and its genetic determinants. It is easy to imagine that varied and far-reaching research programs will develop to interpret and fully understand the results of this investigation. The first step of all these studies is the identification of loci, referred to as expression quantitative trait loci, eQTL, tha influence the expression of one or more genes in one or more tissues. This proposal focuses on providing cutting edge computational and statistical tools that will allow identification of eQTL with high sensitivity and at a low false positive rate, maximizing the yield of the GTEx sample collection and experimental studies. We will tackle in turn 1) the development of powerful and sensitive tests; 2) the control of false positives using statistical methodology; and 3) the analyss of replicability and relevance of the results using independent data sets. Specifically, we will develop, implement in software to be freely distributed, and apply to GTEx data, test statistics that are robust to batch effect and population stratification, but adaptable to the coordinated analysis of expression in multiple tissues, allowing for the borrowing of information across tissues. We will establish thresholds for statistical significance of these test statistics using ad extending cutting-edge approaches that will be both mindful of the extremely large number of hypotheses explored and adaptive to the noise structure of the GTEx data. Finally, we will analyze three independent data sets that include genome-wide genotype and expression information as well as rich phenotypic data: this step will allow us to evaluate the reproducibilit of our results, as well as to investigate how the identified eQTLs relate to high-order phenotypes. Comprising investigators at Stanford and UCLA, our team has a record of collaboration and excellence in each of the areas that represent important challenges presented by this project.