LABORATORY, DATA ANALYSIS, AND COORDINATING CENTER (LDACC) OPTION PERIOD 2 (PERIOD OF PERFORMANCE: JANUARY 31, 2014 - JANUARY 30, 2017)

基因类型-组织表达(GTEx)项目是美国国立卫生研究院共同基金倡议(http://nihroadmap.nih.gov/GTEx/)，)的一个项目，旨在为科学界提供一个资源，用于研究遗传变异和基因表达调控之间的关系。该项目将收集和分析来自捐赠者的多个人类组织，这些捐赠者通过密集的基因分型确定了生殖系遗传变异的特征。通过将全球RNA表达水平视为数量性状，与表达变化高度相关的多态基因座将被识别为表达数量性状基因座，简称eQTL。EQTL中与基因表达相关的SNPs有时被称为eSNPs。根据对单个组织的分析，大约4%-12%的基因转录本具有cis-eQTL，操作上定义为位于与其表达相关的基因附近的ESNP。为了识别反式eQTL，即ESNP定位到远离基因或位于与其调控的基因不同的染色体上，将需要比顺式eQTL大得多的样本量。这是因为需要对搜索每个转录本的表达水平与非常大量的遗传变异之间的相关性所涉及的大量统计测试进行调整。对顺式和反式eQTL的全面鉴定将为研究基因调控提供有价值的基础，并立即应用于解释全基因组关联研究的结果。

The Genotype-Tissue Expression (GTEx) project, a NIH Common Fund Initiative (http://nihroadmap.nih.gov/GTEx/), aims to provide a resource to the scientific community with which to study the relationship between genetic variation and regulation of gene expression. This project will collect and analyze multiple human tissues from donors who have been characterized for germline genetic variation through dense genotyping. By treating global RNA expression levels as quantitative traits, loci with polymorphisms that are highly correlated with variations in expression will be identified as expression quantitative trait loci, or eQTLs. The SNPs within the eQTL that are correlated with gene expression are sometimes called eSNPs. Based on analysis of individual tissues, approximately 4-12% of gene transcripts have cis-eQTLs, operationally defined as an eSNP that is located close to the gene whose expression it is correlated with. To identify trans-eQTLs, in which the eSNP maps far from the gene or on a different chromosome than the gene it is regulating, much larger sample sizes relative to cis-eQTLs, will be required. This is because of the need to adjust for the large number of statistical tests involved in searching for correlation between expression levels of every transcript and a very large number of genetic variants. Comprehensive identification of both cis- and trans-eQTLs will provide a valuable basis on which to study gene regulation, with an immediate application in interpreting genome-wide association studies findings.