Abstract
OBJECTIVES: To construct multi-trait polygenic scores (PRS) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validate their performance in diverse cohorts, and identify PRS-related proteins for potential therapeutic targeting. DESIGN: Prospective cohort studies. SETTING: Genetic Epidemiology of COPD (COPDGene; 2007-present), Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE; 2005-2008), Mass General Brigham Biobank (MGBB; 2010-present), All of Us (2016-present), and UK Biobank (UKB; 2006-present). PARTICIPANTS: 6,647 non-Hispanic White (NHW) and 2,466 African American (AA) participants from COPDGene; 1,858 participants from ECLIPSE; 118,566 from All of Us; 15,142 from MGBB with genetic data. 5,173 COPDGene and 5,012 UKB participants with proteomic data. MAIN OUTCOME MEASURES: COPD status (GOLD 2-4 vs. GOLD 0) and COPD exacerbation frequency. RESULTS: PRSmix+, a multi-trait PRS framework, selected 7 traits for a composite PRS (PRS(multi)). In multivariable models, PRS(multi) was associated with COPD status (meta-analysis random effects (RE) OR 1.58 [95% CI: 1.28-1.94]) and exacerbation frequency (meta-analysis RE beta 0.21 [95% CI: 0.11-0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRS(multi) outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRS that were also validated with measured protein levels in COPDGene and UK biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include AGER (RAGE), IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations. CONCLUSIONS: Multi-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management. TRIAL REGISTRATION: COPDGene: NCT00608764; ECLIPSE: NCT00292552.