Multi-tissue and network models for next-generation EQTL studies

项目摘要 这项研究提案的长期目标是提供一个框架，并提供统计和计算 工具，以促进分析和理解表达数量性状基因座(EQTL)在单个和 多组织研究，并阐明组织间差异的基因基础。该提案是 分为四个目标：(1)根据对下列网络的分析开发统计工具的两部分扩展 可加强远端(反式)eQTL的识别；(2)开发快速eQTL的新统计方法 关联作图，提供可靠的效应大小估计；(3)扩展我们现有的多组织eQTL 程序进入高级组织建模平台，能够处理具有20到30个组织的现有数据集； (4)建立基于基因的eQTL统计模型。拟议方法的发展将 由最近的大规模eQTL研究推动，研究人员在其中发挥了关键作用。由此产生的 计算工具将解决这些新数据集分析中当前的关键缺陷，并将 对更广泛的eQTL分析社区具有广泛的实用价值。

Project Summary The long-term objective of this research proposal is to provide a framework, and statistical and computational tools, to advance the analysis and understanding of expression quantitative trait loci (eQTL) in single and multi-tissue studies, and to elucidate the genotypic basis of differences between tissues. The proposal is divided into four Aims: (1) to develop bipartite extensions of statistical tools from the analysis of networks that can enhance the identification of distal (trans) eQTLs; (2) to develop new statistical methods for fast eQTL association mapping that provide reliable estimates of effect size; (3) to extend our existing multi-tissue eQTL procedure into a High-Tissue modeling platform capable of handling existing data sets with 20 to 30 tissues; and (4) to develop gene-based statistical models for eQTL analysis. Development of the proposed methods will be driven by recent, large-scale eQTL studies in which the investigators have played key roles. The resulting computational tools will address current, critical shortcomings in the analysis of these new data sets, and will have broad utility for the wider eQTL analysis community.