Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality worldwide. While cigarette smoking is the major environmental risk factor in developed countries, it fails to adequately explain the variability in development of COPD. We and others have found that a substantial portion of COPD risk is due to genetic factors. To discover these genetic risk factors, we combined results from the UK Biobank with the International COPD Genetics Consortium, totalling 35,735 cases and 222,076 controls. We identified 82 genome-wide significant loci, including 35 novel findings, 13 of which were also associated with lung function in an independent sample. Using data on gene regulation in lung tissues, we found supportive evidence for specific genes and lung cell types, and for different effects on specific lung phenotypes from CT scans. We also found 14 COPD loci shared with either asthma or pulmonary fibrosis. Our analyses provide further support to the genetic susceptibility and heterogeneity of COPD.
Genome-wide integrative and network-based approaches to obstructive lung diseases and their comorbidities
Chronic obstructive pulmonary disease (COPD) and asthma are the two most common obstructive lung diseases in the world, and cause an enormous burden to society. These diseases have unique but also shared characteristics and risk factors. The goal of this proposal is to leverage our expertise in lung disease, genetics, and network modeling to find new unique and shared genetic variants for these diseases. This research will contribute to knowledge about the heterogeneity and susceptibility to two major obstructive lung diseases and their comorbidities. We will obtain data on genetics, lung function, body measurement and composition, and co-morbid diseases (such as cardiac disease) from the UK Biobank. We will combine this data with our existing data COPD, asthma, and comorbidities, to examine genetics common or distinct to asthma, COPD, and specific comorbidities, such as obesity and wasting. Finally, we will use network methods to build disease modules and identify shared and distinct networks. We will analyze the full cohort with available genetic and phenotypic measurements.
|Lead investigator:||Michael Cho|
|Lead institution:||Brigham and Womens Hospital|