Genetic Regulation of Gene Expression and its Impact on Phenotypes - Supplement

描述(由申请人提供)： 项目概述基因类型-组织表达(GTEx)计划正在投入大量资源收集数据集，该数据集有望提供前所未有的机会来了解人类基因表达的调节，它在组织中的调节，以及它的遗传决定因素。不难想象，将制定各种影响深远的研究计划，以解释和充分理解这项调查的结果。所有这些研究的第一步是确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)。这项建议侧重于提供尖端的计算和统计工具，使eQTL能够以高灵敏度和低假阳性率识别，最大限度地提高GTEx样本收集和实验研究的产量。我们将依次处理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重复性和相关性。具体地说，我们将开发、实现在可自由分发的软件中，并将其应用于GTEx数据，测试统计数据，这些统计数据对批量效应和种群分层具有健壮性，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。我们将使用和扩展尖端方法来确定这些测试统计数据的统计意义的阈值，这些方法将既考虑到所探索的极大量的假设，又适应GTEx数据的噪声结构。最后，我们将分析三个独立的数据集，其中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结果的重复性，以及调查已识别的eQTL如何与高阶表型相关。由斯坦福大学和加州大学洛杉矶分校的研究人员组成，我们的团队在代表该项目提出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.