Statistical analysis of gene expression quantitative trait loci (eQTL)

基因表达数量性状位点（eQTL）的统计分析

• 批准号: 8586067
• 负责人: MATTHEW STEPHENS
• 金额: $ 39.23万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8586067
• 关键词: Address; Binding Sites; Biological; Biology; Cell Line; Communities; Computing Methodologies; Data; Data Analyses; Deoxyribonucleases; Development; Disease; Enrollment; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genetic; Genetic Variation; Genomics; Genotype; Goals; Histocompatibility Testing; Human; Human Herpesvirus 4; Individual; Internet; Lead; Maps; Methods; Modeling; Nature; Output; Paper; Pilot Projects; Publishing; Quantitative Trait Loci; Research Infrastructure; Research Personnel; Resources; Scientist; Secure; Signal Transduction; Site; Source; Statistical Methods; Tissue Sample; Tissues; Variant; base; cell type; computer infrastructure; experience; falls; genetic variant; genome wide association study; human disease; improved; lymphoblastoid cell line; member; novel; public health relevance; tool; transcription factor; transcriptome sequencing; treatment strategy; working group

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.

描述（由申请人提供）：近年来，将遗传变异与基因表达相关的研究（“ EQTL研究”）已成为鉴定调节遗传变异的主要工具。但是，确保原代组织样品的困难意味着到目前为止，这些EQTL研究已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转化的淋巴细胞细胞系中进行的，目前尚不清楚这些细胞系中确定的EQTL在多大程度上与人类疾病映射有关。 GTEX项目将提供数据来纠正这种情况，收集数百个个体的30个组织的RNA-Seq和基因型数据。但是，当前的分析工具的充分利用这些工具的能力有限 数据。特别是，可用的方法缺乏它们共同分析所有TSSUS数据以最大化功率的能力，同时允许每个组织中存在的EQTL之间的差异。在这里，我们建议开发新颖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（例如从编码项目中获得的数据）集成在一起，并以方便的形式在Internet上传播结果。我们还将为研究人员提供方便的工具，以通过全基因组关联研究的结果为GTEX项目的交叉引用结果。该项目的总体目标是建立和应用基础架构以改进EQTL分析，从而最大程度地提高GTEX数据的实用性和可访问性，向希望使用这些数据的广泛的科学家社区。