FACILITATING GTEx, DISEASE, AND GxE ANALYSES VIA FAST EXPRESSION (e)QTL MAPPING

描述（由申请人提供）： 本申请涉及RFA RM-09-006：人类基因表达数量性状基因座（eQTL）分析的新型统计方法。全基因组关联研究（GWAS）正迅速成为发现表型-基因型关联的优选方法;然而，候选物的统计功效、复制和验证仍然具有挑战性。除了基因型，基因表达数据沿着疾病和暴露数据，目前正在收集大量人类队列、多种组织和动物实验中的数据。我们将检验以下假设：表达数量性状基因座（eQTL）分析是一种有效的和机制相关的方法，用于发现和验证控制生物途径和网络的候选基因组基因座/基因，使用来自各种组织、来自疾病与正常条件或在实验扰动下的表达数据。最终目标是阐明人类疾病的基础。在这个项目中，我们将开发新的统计工具和图形用户界面软件来处理这些不同的数据流。分析的主要目标是确定遗传多态性，表达和组织类型或表型之间的相互作用，这是使用传统GWAS无法发现的。我们已经组建了一个由生物医学科学家、统计遗传学家和统计学家组成的经验丰富的团队，我们已经为统计方法奠定了方法学和计算基础。此外，我们拥有成功的软件开发记录，我们已经开始构建面向广大科学界的用户友好型eQTL软件。我们描述了如何将eQTL定位应用于大规模GWAS研究中的一些关键的剩余挑战将在两年的时间内得到解决：（i）在大的纯合和杂合群体中进行快速和统计上严格的eQTL分析;（ii）开发快速的基于ANOVA的表达模型作为基因型和组织类型的函数;（iii）将表型性状建模为表达和基因型的函数;（iv）将表型性状建模为表达和基因型的函数。和（iv）使用双聚类识别显著个体-转录本关联的模式。 公共卫生相关性： 我们提出了一个为期两年的计划，以开发新的统计工具和图形用户友好的软件，以促进eQTL研究的分析。该提案对RFA高度响应，有针对多个组织来源的具体计划（如GTEx数据）和结合疾病表型，基因型和表达的研究。该项目将为阐明复杂的生物学基础疾病创造有效的工具。

DESCRIPTION (provided by applicant): This application addresses RFA RM-09-006: Novel statistical methods for human gene expression quantitative trait loci (eQTL) analysis. Genome-wide association studies (GWAS) are rapidly becoming the preferred approach for discovery of phenotype- genotype associations; however, statistical power, replication and validation of candidates remain to be challenging. In addition to genotypes, gene expression data are now being collected along with disease and exposure data in large human cohorts, across multiple tissues, and in animal experiments. We will test the hypothesis that expression quantitative trait locus (eQTL) analysis is an effective and mechanistically- relevant approach to the discovery and validation of candidate genomic loci/genes that control biological pathways and networks, using expression data from various tissues, from disease vs. normal conditions, or under experimental perturbation. The ultimate goal is to elucidate the underpinnings of human disease. In this project we will develop new statistical tools and graphical user interface-enabled software to handle these diverse data streams. The primary goal of the analysis is to identify the interactions among genetic polymorphisms, expression, and tissue type or phenotype, which would not be found using traditional GWAS. We have assembled an experienced team of biomedical scientists, statistical geneticists, and statisticians, and we already laid out the methodological and computational groundwork for the statistical approaches. In addition, we have a track record of successful software development, and we have already begun building user-friendly eQTL software aimed at the broad scientific community. We describe how a number of key remaining challenges in applying eQTL mapping to large-scale GWAS studies will be addressed in a two-year period by: (i) enabling fast and statistically rigorous eQTL analyses in large homo- and hetero-zygous populations; (ii) developing fast ANOVA-based modeling of expression as a function of genotype and tissue type; (iii) modeling phenotypic traits as a function of expression and genotype; and (iv) indentifying patterns of significant individual-transcript associations using biclustering. PUBLIC HEALTH RELEVANCE: PROJECT NARRATIVE We present a two-year plan to develop new statistical tools and graphical user-friendly software to facilitate the analysis of eQTL studies. The proposal is highly responsive to the RFA, with specific plans to address multiple tissue sources (as with GTEx data) and studies combining disease phenotype, genotype and expression. The project will create effective tools for elucidating the complex biology underlying disease.