INTEGRATIVE GENOMICS OF CLINICAL SUBTYPES IN COPDGENE

 描述(申请人提供)：慢性阻塞性肺疾病(COPD)是一种异质性疾病，不能通过肺活量测定法测量的气流受限程度来检测。COPD遗传流行病学研究的目标之一是定义COPD有意义的亚组，导致新的疾病分类。这些临床和统计方法利用了COPDgene收集的大量表型数据，包括胸部CT扫描。与主要的COPD基因研究不同，《COPD基因临床亚型的整合基因组学》研究将使用RNA测序和miRNA进行亚型分析。假设不同的COPD亚型会有不同的病理生理学，这可以通过基因表达特征、miRNA图谱和综合基因组学研究来识别。全基因组的基因分型数据可以用来测试与亚型的关联。然而，传统的全基因组关联研究可能不足以检测亚型效应。基因表达是介于基因类型和复杂性状之间的一种重要的中间表型，在外周血中很容易检测到。在这个方案中，我们将使用表达数量性状基因座(EQTL)分析来确定影响差异表达基因和miRNAs表达的功能性单核苷酸多态(SNPs)。我们将致力于以下具体目标：(1)COPD亚型的基因表达谱：我们将收集COPD基因受试者的外周血液RNA样本，进行RNA测序和miRNA谱分析，并测试差异表达的转录本和miRNAs，以进行两种临床亚型比较：(A)以肺气肿为主的COPD与以呼吸道为主的COPD以及(B)频繁和罕见的急性加重。我们将通过对COPD肺组织样本进行RNA测序来验证血液相关性。(2)COPD分子亚型：我们将基于RNA测序和miRNA数据，使用统计和机器学习方法来确定分子亚型。我们将通过临床、影像和纵向随访数据来验证分子亚型。(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，以确定与差异表达和亚型定义基因的转录水平相关的eQTL SNP，以及AIMS 1和AIM 2中的miRNAs。eQTL SNPs将在整个COPD基因研究人群中进行与临床亚型的相关性测试。这项建议将是独特的，但又是对母公司COPD基因研究的补充，通过使用mRNA和miRNA表达数据来确定对COPD亚型的遗传影响，这可以确定疾病亚型的生物标记物或新的途径和靶点，朝着COPD精确医学的目标迈进。基因表达、miRNA和eQTL数据集将作为COPDgene和COPD研究人员社区的资源。

 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.