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相关联的研究（“eQTL研究”）已成为鉴定调控遗传变异的主要工具。然而，获得原代组织样品的困难意味着到目前为止，这些eQTL研究已经在有限的细胞和组织类型范围内进行。最值得注意的是，已经在EBV转化的淋巴母细胞系中进行了最大规模的研究，并且尚不清楚在这些细胞系中鉴定的eQTL将在多大程度上与人类疾病作图相关。GTEx项目将提供数据来纠正这种情况，收集数百人30种组织的RNA-seq和基因型数据。然而，目前的分析工具在充分利用这些资源的丰富性方面受到限制。 数据特别是，可用的方法在联合分析所有组织的数据以最大化功效，同时允许存在于每个组织中的eQTL之间的差异的能力不足。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确定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，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，以交叉引用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一个基础设施，以改进eQTL分析，帮助最大限度地提高GTEx数据的实用性和可访问性，使广大科学家能够使用这些数据。

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.