Genomic and Transcriptomic Analysis of Emphysema and Subclinical ILD

 描述(申请人提供)：肺气肿和慢性阻塞性肺疾病(COPD)加在一起是美国的第三大死因。虽然肺气肿通常被认为是一种疾病，但在计算机断层扫描(CT)上，肺气肿是一个独特的实体，在一些COPD患者中缺失，在一些没有COPD的患者中存在。间质性肺病是一类以肺泡损伤、炎症和纤维化为特征的非感染性、非恶性肺部疾病。目前几乎没有药物可以阻止这些疾病的发展，这反映出对潜在的分子机制的了解有限。我们最近完成了来自动脉粥样硬化多种族研究(MESA)的约7600名参与者在CT扫描上对肺气肿百分比和亚临床ILD的全基因组关联研究(GWAS)。我们的Gwas努力在SNRPF或其附近识别具有全基因组意义的SNPs和肺气肿百分比的PPT2，以及亚临床ILD性状的ANRIL和D21S2088E的总体成功反映了MESA肺表型的显著质量。尽管如此，Gwas方法继续面临多重限制，包括(A)考虑数百万个SNP带来的巨大多重测试负担， 以及(B)缺乏将已识别的SNPs与特定基因联系起来的功能注释。为了解决这些和其他限制，我们建议应用一种新的基于基因的关联方法，称为PrediXcan，它直接测试遗传变异影响表型的分子机制。该方法估计由个体的遗传特征决定的基因表达的组成部分，并将“归因于”的基因表达与所研究的表型相关联，以确定与表型的病因学有关的基因。PrediXcan的框架为MESA参与者“推算”特定于组织的全基因组基因表达水平提供了可能，从而创建了一个基于人群的数据集，将CT扫描中肺气肿百分比的高质量表型与全血和肺特定基因表达水平结合在一起。我们预计，通过PrediXcan获得的基因表达特征减少了多重测试负担，增加了功能相关性，将使我们能够识别与肺气肿和ILD相关的新基因和途径。因此，我们建议开展MESA中肺气肿百分比(目标1a)和亚临床ILD(目标1b)特征的基因表达预测因子的转录组研究。我们进一步使用模型选择框架来确定普通人群中肺气肿和亚临床ILD发病机制的基因组合(目标2)。我们已经组建了一个高度协作和跨学科的研究团队，代表着统计遗传学(Manichaikul)方面的专业知识 和IM)、遗传流行病学(Manichaikul和Rich)、肺流行病学(Leder和Barr)、肺病理学(Borczuk)和系统遗传学(Farber)。完成拟议的目标将有助于更好地了解与肺气肿和ILD相关的预测基因表达特征，从而改进对这些疾病的有针对性的预防和治疗。

 DESCRIPTION (provided by applicant): Together, pulmonary emphysema and chronic obstructive pulmonary disease (COPD) are the third leading cause of death in the United States. Although often considered one disease, emphysema on computed tomography (CT) represents a distinct entity that is absent in some patients with COPD and present in some without COPD. The interstitial lung diseases (ILDs) are a class of non-infectious, non-malignant lung diseases characterized by alveolar injury, inflammation, and fibrosis. There are currently few medications available to stop the progression of these diseases, reflecting a limited understanding of the underlying molecular mechanisms. We recently completed genome-wide association studies (GWASs) of percent emphysema and subclinical ILD on CT scan in ~7,600 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). The overall success of our GWAS efforts in identifying SNPs at genome-wide significance in or near SNRPF and PPT2 for percent emphysema, and ANRIL and D21S2088E for subclinical ILD traits reflect the notable quality of the pulmonary phenotypes in MESA. Still, GWAS approaches continue to face multiple limitations including (a) large multiple testing burden from considering millions of SNPs, and (b) lack of functional annotation to connect identified SNPs with specific genes. To address these and other limitations, we propose to apply a new gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates the "imputed" gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The framework of PrediXcan opens the possibility to "impute" tissue-specific genome-wide gene expression levels for the MESA participants, thereby creating a population-based data set that combines both high quality phenotypes for percent emphysema on CT scan with whole blood and lung-specific gene expression levels. We expect that reduced multiple testing burden and increased functional relevance of gene expression traits obtained through PrediXcan will allow us to identify novel genes and pathways related to emphysema and ILD. Therefore, we propose to carry out transcriptome-wide studies of gene expression predictors for percent emphysema (Aim 1a) and subclinical ILD (Aim 1b) traits in MESA. We further use a model selection framework to identify combinations of genes underlying pathogenesis of emphysema and subclinical ILD in the general population (Aim 2). We have assembled a highly collaborative and interdisciplinary team of investigators representing expertise in statistical genetics (Manichaikul and Im), genetic epidemiology (Manichaikul and Rich), pulmonary epidemiology (Lederer and Barr), lung pathology (Borczuk) and systems genetics (Farber). Completion of the proposed Aims would result in improved understanding of predicted gene expression traits in relation to emphysema and ILD, leading to improved targeted prevention and treatment of these diseases.