Statistical analysis of gene expression quantitative trait loci (eQTL)

描述(申请人提供)：近年来，将遗传变异与基因表达联系起来的研究(“eQTL研究”)已成为识别调节遗传变异的主要工具。然而，获取初级组织样本的困难意味着到目前为止，这些eQTL研究只在有限的细胞和组织类型中进行。最值得注意的是，最大的研究是在EBV转化的淋巴母细胞系中进行的，目前尚不清楚在这些细胞系中发现的eQTL将在多大程度上与人类疾病图谱相关。GTEx项目将提供数据来补救这种情况，收集数百人30个组织的RNA-seq和基因数据。然而，当前的分析工具在充分利用这些信息的丰富性方面的能力有限 数据。特别是，现有的方法在联合分析所有组织上的数据以最大化功率的能力方面存在不足，同时允许每个组织中存在的eQTL之间的差异。在这里，我们建议开发新的统计方法来帮助解决这些问题。我们将应用这些方法来识别GTEx项目数据中的eQTL，将GTEx数据与其他相关数据(如ENCODE项目提供的数据)整合在一起，并以方便的形式在互联网上发布结果。我们还将为研究人员提供方便的工具，以交叉参考GTEx项目的结果与全基因组关联研究的结果。该项目的总体目标是建立和应用改善eQTL分析的基础设施，帮助想要使用这些数据的广大科学家最大限度地提高GTEx数据的实用性和可及性。

DESCRIPTION (provided by applicant): In recent years, studies that associate genetic variation with gene expression ("eQTL studies") have become a major tool for identifying regulatory genetic variation. However, the difficulty of securing primary tissue samples means that up to now these eQTL studies have been conducted in a limited range of cell and tissue types. Most notably the largest studies have been conducted in EBV-transformed lymphoblastoid cell lines, and it is unclear to what extent eQTLs identified in these cell lines wil be relevant to human disease mapping. The GTEx Project will provide data to remedy this situation, collecting RNA-seq and genotype data on 30 tissues in hundreds of individuals. However, current analytic tools are limited in their ability to fully exploit the richness of these data. In particular, available methods fall short in their ability to jointly analyze data on all tssues to maximize power, while simultaneously allowing for differences among eQTLs present in each tissue. Here we propose to develop novel statistical methods to help address these issues. We will apply these methods to identify eQTLs in the GTEx project data, integrate the GTEx data with other relevant data such as those available from the ENCODE project, and disseminate the results on the internet in a convenient form. We will also provide researchers with convenient tools to cross-reference results of the GTEx project with results of genome-wide association studies. The overall goal of the project is to build and apply an infrastructure for improved eQTL analyses, helping to maximize the utility and accessibility of GTEx data to the broad community of scientists who would like to use these data.