Deciphering respiratory disease mechanisms through integration of genomic and functional data across massive global biobanks

About This Grant

PROJECT ABSTRACT/SUMMARY Chronic obstructive pulmonary disease (COPD) and asthma are the two most common chronic respiratory diseases, with COPD as the third leading cause of death globally and asthma as the most common chronic disease of childhood. They are treatable, heritable, strongly affected by the environment (e.g. smoking, pollution), and exhibit some of the largest disparities in prevalence and survival among populations. Genetic and multi-omic technologies provide powerful opportunities to identify both shared and distinct heritable risk factors among asthma and COPD, connect those genetic variants with function, and delineate relationships between genetic, environment, and disease risk using multi-omic molecular assays. However, most genetic studies of lung diseases and comorbid traits to date have focused narrowly on one or few traits at a time without modeling the dense correlation between risk factors and have been vastly Eurocentric. This is problematic, as minority communities who face a higher burden of respiratory diseases disproportionately experience adverse exposures that can be challenging to study because they are often imprecisely measured, and the level of exposure that is biologically meaningful for disease onset and progression is unclear. In this R01 application, we will integrate multi-trait, multi-ancestry, and multi-omic data from massive biobanks. We will bring together genetic and phenotypic data across the All of Us Research Program, Million Veterans Program, UK Biobank, FinnGen, and other global biobanks, totaling more than 2 million participants, including approximately 240,000 COPD cases, 260,000 asthma cases, and 60,000 proteomic profiles. In Aim 1, we will conduct powerful genetic studies of lung diseases and comorbidities, model genetic correlations among related traits and ancestries to maximize genomic discovery, refine putative causal variants, and predict genetic risk accurately across populations. In Aim 2, we will connect asthma and COPD genetic risk loci to function by using their pleiotropic relationships to dissect them into sets with similar effects, then test for enrichments and colocalizations among these sets of variants with multi-omic molecular assays to elucidate their molecular mechanisms. Lastly, in Aim 3, we will bridge the gap between inherited and environmental influences to better understand phenotypic heterogeneity in asthma and COPD by leveraging proteomic and metabolomic profiles to develop scores that predict incident disease and well-known risk factors. This R01 application will deliver accurate and reproducible genetic, environmental, and multi-omic molecular predictors of asthma and COPD that will identify causal mechanisms and modifiable risk factors. Quantifying the relative importance of risk factors as proposed here is critical for advancing clinical models, nominating targeted interventions to improve public health, and ultimately reducing vast health disparities in lung disease.