Using Integrative Genomics To Identify and Characterize Emphysema-Associated eQTL

描述（由申请人提供）：慢性阻塞性肺疾病（COPD）是一种主要的公共卫生负担，是美国第三大死亡原因。COPD全基因组关联研究（GWAS）已成功地确定了疾病相关的遗传位点，但大多数对这种疾病的遗传易感性尚未得到解释。将GWAS发现转化为新的疾病修饰治疗需要继续发现GWAS鉴定的基因座并对其进行功能表征。很明显，许多GWAS基因座是基因表达数量性状基因座（即eQTL），基因表达模式的精细调节是复杂疾病（包括COPD）遗传结构的关键组成部分。COPD是一种复杂的临床综合征，可能由不同的生物学过程组成。解决这种复杂性的一个富有成效的方法是鉴定COPD相关的表型，例如肺气肿，它们具有自己的强GWAS信号和独特的生物学。肺气肿是一种可遗传的表型。然而，很少有研究能够在足够大的样本中产生肺气肿表型，以获得足够的动力GWAS，并且现有的定量肺气肿测量具有已知的局限性。使用一种新的方法，肺气肿量化从肺部计算机断层扫描的基础上，局部直方图，我们已经产生了详细的定量肺气肿措施，超过9,000名受试者在COPDGene研究，比传统的肺气肿措施更有信息。该建议基于两个假设- 1）局部直方图肺气肿模式的GWAS将鉴定新的肺气肿相关遗传变体，以及2）基因表达的遗传控制是遗传变异影响肺气肿表型的重要机制。为了研究这些假设，我们提出了以下具体目标。在目标1中，将在COPDGene和ECLIPSE研究中生成更新的LHE表型，并将进行GWAS以识别肺气肿相关的遗传变异。在目标2中，将在来自具有一系列肺气肿的COPDGene受试者的支气管上皮细胞（BEC）和全血样品中使用RNA-Seq数据进行eQTL分析。这些eQTL和GWAS结果将被整合以鉴定新的肺气肿相关基因座，并将这些基因座与它们发挥表型效应的基因联系起来。在Aim 3a中，我们将通过整合来自ENCODE和Roadmap表观基因组学项目的GWAS、eQTL和功能调控数据的创新方法，鉴定可能与肺气肿易感性有因果关系的基因组区域。在目标3b中，将在BEC细胞系中基于细胞的功能测定中检查这些基因组区域的功能相关性。这项工作建立在强有力的初步数据基础上，研究团队在该项目的关键领域拥有密切合作和多学科专业知识的历史。

DESCRIPTION (provided by applicant): Chronic obstructive pulmonary disease (COPD) is a major public health burden, and it is the third leading cause of death in the US. Genome-wide association studies (GWAS) in COPD have successfully identified disease-associated genetic loci, but most of the genetic susceptibility to this disease has yet to be explained. The translatin of GWAS discoveries into new disease-modifying treatments requires the continued discovery and functional characterization of GWAS-identified loci. It is clear that many GWAS loci are gene expression quantitative trait loci (i.e. eQTLs) and that fine modulation of gene expression patterns is a key component of the genetic architecture of complex diseases, including COPD. COPD is a complex clinical syndrome that may consist of distinct biological processes. One fruitful approach to this complexity is to identify COPD-associated phenotypes, such as emphysema, that have their own strong GWAS signals and distinct biology. Emphysema is a heritable phenotype. However, few studies have been able to generate emphysema phenotypes in samples large enough for adequately-powered GWAS, and existing quantitative emphysema measures have known limitations. Using a novel, approach for emphysema quantification from lung computed tomography scans based on local histograms, we have generated detailed quantitative emphysema measures on over 9,000 subjects in the COPDGene Study that are more informative than traditional emphysema measures. This proposal is based on two hypotheses - 1) GWAS of local histogram emphysema patterns will identify novel emphysema-associated genetic variants and 2) genetic control of gene expression is an important mechanism by which genetic variation impacts the emphysema phenotype. To investigate these hypotheses, we propose the following specific aims. In Aim 1, updated LHE phenotypes will be generated in the COPDGene and ECLIPSE studies, and GWAS will be performed to identify emphysema-associated genetic variants. In Aim 2, eQTL analysis using RNA-Seq data will be performed in bronchial epithelial cells (BECs) and whole blood samples from COPDGene subjects with a range of emphysema. These eQTL and GWAS results will be integrated to identify novel emphysema-associated loci and link these loci to the genes through which they exert their phenotypic effects. In Aim 3a, we will identify genomic regions that are likely to be causally-linked to emphysema susceptibility through an innovative approach integrating GWAS, eQTL, and functional regulatory data from the ENCODE and Roadmap Epigenomics projects. In Aim 3b, the functional relevance of these genomic regions will be examined in cell-based functional assays in BEC cell lines. This work builds on strong preliminary data, and the research team has a history of close collaboration and multidisciplinary expertise in areas critical for this project.