NHLBI TOPMed: Lung Tissue Research Consortium (LTRC)

Chronic obstructive pulmonary disease (COPD), a disease state characterized by airflow limitation that is not fully reversible, is the third leading cause of death in the U.S. COPD is a heterogeneous syndrome, with affected individuals demonstrating marked differences in lung structure (emphysema vs. airway disease); physiology (airflow obstruction); and other clinical features (e.g., exacerbations, co-morbid illnesses). Multiple genomic regions influencing COPD susceptibility have been identified by genome-wide association studies (GWAS), and rare coding variants can also influence risk for COPD. However, only a small percentage of the estimated heritability for COPD risk can be explained by known genetic loci. Like most complex diseases, COPD is influenced by multiple genetic determinants (each with modest individual effects). Emerging evidence supports the paradigm that complex disease genetic determinants are part of a network of interacting genes and proteins; perturbations of this network can increase disease risk. To identify this network, multiple Omics data will need to be analyzed with methods to account for nonlinear relationships and interactions between key genes and proteins. Our overall hypothesis is that integrated network analysis of genetic, transcriptomic, proteomic, and epigenetic data from biospecimens ranging from lung tissue to nasal epithelial cells to blood in highly phenotyped subjects will provide insights into COPD pathogenesis and heterogeneity. We will leverage the well-phenotyped, NHLBI-funded Lung Tissue Research Consortium (LTRC) to address these questions. We will perform multi-omics analysis in 1548 lung tissue and blood samples from the LTRC. With these multi-omics data, we will utilize a systems biology approach to understand relationships between multiple genetic determinants and multiple types of Omics data. We will begin by performing single Omics analyses in COPD vs. control lung, nasal, and blood samples. Next, we will integrate single Omics data with genetic variants identified by WGS to assist in fine mapping genetic determinants of COPD. We will then perform integrated network analysis of COPD with genetic and multiple Omics data using correlation-based, gene regulatory, and Bayesian networks. Subjects were recruited from Mayo Clinic, Universities of Colorado, Michigan, and Pittsburgh, and Temple University.

慢性阻塞性肺疾病（Chronic Obstructive Pulmonary Disease, COPD）是一种以不完全可逆性气流受限为特征的疾病状态，现为美国第三大死亡病因。COPD属于异质性综合征，患者在肺结构（肺气肿vs.气道疾病）、病理生理（气流阻塞）及其他临床特征（如急性加重、合并症等）方面均存在显著差异。全基因组关联研究（Genome-Wide Association Studies, GWAS）已鉴定出多个影响COPD易感性的基因组区域，罕见编码变异同样可影响COPD患病风险。然而，目前已知的遗传位点仅能解释COPD患病风险中极小一部分的估计遗传力。与多数复杂疾病类似，COPD受多种遗传决定因素共同影响（每种因素的个体效应均较弱）。日益增多的证据支持下述范式：复杂疾病的遗传决定因素是相互作用的基因与蛋白质网络的组成部分，该网络的扰动可增加疾病患病风险。为鉴定该网络，需采用能够考量关键基因与蛋白质间非线性关系及相互作用的分析方法，对多组学数据进行整合分析。本研究的总体假说为：对表型特征明确的受试者的各类生物样本（涵盖肺组织、鼻上皮细胞及血液）开展遗传数据、转录组数据、蛋白质组数据及表观遗传数据的整合网络分析，可阐明COPD的发病机制与异质性。本研究将依托表型特征完备、由美国国立心肺血液研究所（National Heart, Lung, and Blood Institute, NHLBI）资助的肺组织研究联盟（Lung Tissue Research Consortium, LTRC）解决上述科学问题。本研究将对来自LTRC的1548份肺组织与血液样本开展多组学分析。借助上述多组学数据，本研究将采用系统生物学方法，解析多种遗传决定因素与各类组学数据间的关联关系。本研究将首先针对COPD患者与对照人群的肺组织、鼻上皮及血液样本开展单组学分析。随后，本研究将把单组学数据与全基因组测序（Whole Genome Sequencing, WGS）鉴定得到的遗传变异进行整合，以辅助COPD遗传决定因素的精细定位。后续，本研究将采用基于相关性分析、基因调控网络及贝叶斯网络的方法，结合遗传数据与多组学数据开展COPD整合网络分析。本研究的受试者招募自梅奥诊所、科罗拉多大学、密歇根大学、匹兹堡大学及天普大学。