Chronic obstructive pulmonary disease (COPD) is a syndrome commonly defined by a single physiological feature–chronic airflow obstruction. To improve diagnosis and treatment of COPD, it is essential to divide this complex syndrome into biologically meaningful subgroups of COPD patients and to predict disease progression accurately within those subtypes. Our primary approaches to assess COPD progression will use physiology (i.e., FEV1 decline) and imaging (i.e., increased emphysema); however, we will also assess clinical characteristics (e.g., increased exacerbations) and epidemiology (e.g., increased mortality). We will apply imaging, physiology, epidemiology, transcriptomics, proteomics, metabolomics, epigenetics, and genetics to a 15-year longitudinal cohort of smokers to provide new insights into COPD progression. Longitudinal analysis of physiological and imaging data will provide valuable information regarding progression (or stability) of airflow obstruction, CT measurements of emphysema and airway disease, and clinical characteristics. Longitudinal analysis of Omics data will identify biomarkers of COPD progression within disease subtypes. Machine learning and network analysis of multiple Omics data will implicate pathobiological mechanisms for COPD progression. Genetic association analysis using existing whole genome sequencing (WGS) data will identify both rare and common genetic determinants of COPD progression. Integrating WGS with RNA-Seq, DNA methylation, metabolomics, and proteomics will also facilitate identification of functional variants within regions of genetic association.