INTEGRATIVE GENOMICS OF CLINICAL SUBTYPES IN COPDGENE

 描述（由申请人提供）：慢性阻塞性肺疾病（COPD）是一种异质性疾病，无法通过肺量测定法测量的气流受限程度来捕获。COPD遗传流行病学研究（COPDGene）的目标之一是定义有意义的COPD亚组，从而产生新的疾病分类。这些临床和统计方法利用了COPDGene中收集的大量表型数据，包括胸部CT扫描。“COPD基因临床亚型的整合基因组学”研究将使用RNA测序和miRNA进行亚型分型，与主要的COPD基因研究不同。假设不同的COPD亚型将具有不同的病理生理学，这可以通过基因表达特征、miRNA谱和整合基因组学研究来鉴定。全基因组基因分型数据可用于测试与亚型的关联。然而，传统的全基因组关联研究可能不足以检测亚型效应。基因表达是基因型和复杂性状之间的重要中间表型，并且可以容易地在外周血中测定。在本研究中，我们将使用表达数量性状基因座（eQTL）分析来鉴定影响差异表达基因和miRNA表达的功能性单核苷酸多态性（SNP）。我们将解决以下具体目标：（1）COPD亚型的基因表达谱：我们将收集COPD基因中受试者的外周血RNA样本，进行RNA测序和miRNA谱分析，并测试差异表达的转录物和miRNA，用于两种临床亚型比较：（A）肺气肿为主与气道为主的COPD和（B）频繁与罕见的急性加重。我们将通过COPD肺组织样本中的RNA测序来验证血液相关性。(2)COPD的分子亚型：我们将使用统计和机器学习方法来定义基于RNA测序和miRNA数据的分子亚型。我们将结合临床、影像学和纵向随访资料对分子亚型进行验证。(3)COPD亚型的整合基因组学：我们将整合基因和miRNA表达数据与全基因组SNP数据，以鉴定与差异表达和亚型定义基因以及Aim 1和Aim 2的miRNA的转录水平相关的eQTL SNP。将在完整COPDGene研究人群中检测eQTL SNP与临床亚型的相关性。该提案将通过使用mRNA和miRNA表达数据来识别对COPD亚型的遗传影响，从而识别疾病亚型的生物标志物或新的途径和靶点，从而实现COPD精准医学的目标。基因表达，miRNA和eQTL数据集将作为COPDGene和COPD研究者社区的资源。

 DESCRIPTION (provided by applicant): Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease, which is not captured by the degree of airflow limitation measured by spirometry. One of the goals of the Genetic Epidemiology of COPD Study (COPDGene) is to define meaningful subgroups of COPD, leading to a new disease classification. These clinical and statistical approaches are utilizing the extensive phenotype data collected in COPDGene, including chest CT scans. The "Integrative Genomics of Clinical Subtypes in COPDGene" study will use RNA sequencing and miRNA for subtyping, different from the main COPDGene study. The hypothesis is that different COPD subtypes will have distinct pathophysiology, which can be identified through gene expression signatures, miRNA profiling and integrative genomics studies. Genomewide genotyping data can be used to test for associations with the subtypes. However, traditional genomewide association studies may be underpowered to detect subtype effects. Gene expression is an important intermediate phenotype between genotypes and complex traits, and can be easily assayed in peripheral blood. In this proposal, we will use expression quantitative trait locus (eQTL) analysis to identify functional single nucleotide polymorphisms (SNPs) affecting expression of differentially expressed genes and miRNAs. We will address the following Specific Aims: (1) Gene expression profiling in COPD subtypes: We will collect peripheral blood RNA samples from subjects in COPDGene, perform RNA sequencing and miRNA profiling and test for differentially expressed transcripts and miRNAs for two clinical subtype comparisons: (A) emphysema-predominant vs. airway-predominant COPD and (B) frequent vs. infrequent acute exacerbations. We will validate the blood associations by RNA-sequencing in COPD lung tissue samples. (2) Molecular subtypes of COPD: We will use statistical and machine learning methods to define molecular subtypes based on the RNA sequencing and miRNA data. We will validate the molecular subtypes with the clinical, imaging and longitudinal follow-up data. (3) Integrative genomics of COPD subtypes: We will integrate the gene and miRNA expression data with genomewide SNP data to identify eQTL SNPs associated with transcript levels of the differentially expressed and subtype-defining genes and miRNAs from Aims 1 and Aim 2. The eQTL SNPs will be tested for association with the clinical subtypes in the full COPDGene Study population. This proposal will be distinct yet complement the parent COPDGene Study by using mRNA and miRNA expression data to identify genetic influences on COPD subtypes, which could identify biomarkers of disease subtypes or novel pathways and targets, moving towards the goal of precision medicine in COPD. The gene expression, miRNA and eQTL datasets will serve as resources for COPDGene and the community of COPD investigators.