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: 8878358
• 负责人: MATTHEW STEPHENS
• 金额: $ 37.78万
• 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 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8878358
• 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; Health; 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; 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联系起来的研究（“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，最大规模的研究是在ebv转化的淋巴母细胞样细胞系中进行的，目前尚不清楚在这些细胞系中鉴定的eqtl在多大程度上与人类疾病制图相关。GTEx项目将提供数据来纠正这种情况，收集数百个人30个组织的RNA-seq和基因型数据。然而，当前的分析工具在充分利用这些丰富性方面的能力是有限的