Project Summary/Abstract Airflow obstruction is a defining pathophysiologic feature of chronic obstructive pulmonary disease (COPD). COPD affects approximately 28.9 million people and is the 3rd leading cause of death in the United States. Although the main risk factor for COPD is cigarette smoking, there is evidence of genetic susceptibility as well. Monogenic syndromes —Alpha-1 antitrypsin deficiency and cutis laxa— have emphysema, a COPD phenotype, as part of their manifestations. An understudied pathophysiologic feature leading to airflow obstruction in COPD is mucus dysfunction. Cigarette smoke-induced mucus dysfunction results in increased production of and reduced clearance of mucus leading to its accumulation in the airways and plug formation, which in turn leads to airflow obstruction. Lung tissue studies demonstrated that occlusion of small airways by mucous exudates are related to disease severity and mortality, underscoring the clinical relevance of mucus dysfunction. The main limitation of current clinical and genetic studies of mucus dysfunction in COPD is that they have relied on self-reported data such as chronic cough and phlegm and lung tissue to reflect this abnormality. In this proposal we will visually identify and score mucus plugging on chest computed tomography (CT) from subjects enrolled into the COPDGene, Evaluation of COPD Longitudinally to Identify Predictive Surrogate End- points (ECLIPSE) and Detection of Early Lung Cancer Among Military Personnel (DECAMP)-2 studies —large cohorts of smokers with and without COPD. In Aim 1, we will perform a visual scoring in phases 1, 2 and 3 CT scans of the COPDGene Study. We will then determine the factors associated with 10-yr changes in CT- identified mucus plugging as well as the associations of this imaging feature to acute respiratory disease episodes and death. In Aim 2, we will score mucus plugging in all baseline ECLIPSE CT scans and use the scores of COPDGene from Aim 1 to perform genome-wide association studies to determine the common and rare genetic variants related to CT-identified mucus plugging. We will utilize genome-wide genotyping, imputation, and whole-genome sequencing data for gene discovery. We will then test the associations between CT-identified mucus plugging and common and rare genetic variants using meta-analysis in COPDGene and ECLIPSE cohorts. Finally, in Aim 3 we will score mucus plugging on CT scans from smokers enrolled into DECAMP-2 Study. The transcriptomic analysis will be performed in collected bronchial and nasal epithelial cells to identify gene expression differences for imaging-based mucus plugging. We believe that this project will increase the clinical and genetic understanding of mucus dysfunction, a clear gap in COPD, and will provide a validated imaging assessment that can be used for clinical and epidemiologic investigation.