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：用于人类基因表达数量性状基因座(EQTL)分析的新统计方法。全基因组关联研究正在迅速成为发现表型-基因型关联的首选方法；然而，统计能力、候选基因的复制和验证仍然是具有挑战性的。除了基因类型，基因表达数据现在还与疾病和暴露数据一起在大型人类队列、跨多个组织和动物实验中被收集。我们将检验这样一种假设，即表达数量性状基因座(EQTL)分析是一种有效的和机械相关的方法，用于发现和验证控制生物通路和网络的候选基因组基因座/基因，使用来自各种组织、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支持图形用户界面的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、表达和组织类型或表型之间的相互作用，这在传统的GWA中是找不到的。我们组建了一支由生物医学科学家、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论和计算基础。此外，我们有成功的软件开发记录，我们已经开始开发面向广大科学界的用户友好的eQTL软件。我们描述了如何在两年的时间里解决将eQTL定位应用于大规模GWAS研究中的一些关键剩余挑战：(I)在大量的同型和杂合人群中实现快速和统计上严格的eQTL分析；(Ii)发展基于ANOVA的快速表达模型，作为基因和组织类型的函数；(Iii)作为表达和基因的函数的表型特征的建模；以及(Iv)使用双簇识别显著的个体-转录本关联的模式。

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.