Welcome to the Cho and Moll Lab!

at the Channing Division of Network Medicine, Brigham and Women’s Hospital

Our team uses genetic, integrative omic, and machine learning approaches to identify and characterize factors affecting the risk and heterogeneity of chronic respiratory disease.


The overarching goal of our group is to advance the understanding and treatment of respiratory diseases through data analysis of large cohorts, with a focus on genomics in COPD and interstitial lung disease.  We are based in the Channing Division of Network Medicine (CDNM), a research division of the Department of Medicine at Brigham and Women’s Hospital.  We use integrative, network, and machine learning approaches to investigate disease etiology and heterogeneity through the analysis of large-scale epidemiologic, health outcome, spirometry and imaging, genotyping, whole genome/exome sequencing, microRNA sequencing, bulk and single cell RNA sequencing and ATAC-seq, and proteomic data.  We additionally use Omics for risk prediction, endotyping, and systems pharmacology efforts to identify new therapeutic targets and drug repurposing candidates. In addition to COPD and ILD, we also have projects in mucous plugging, bronchiectasis, and asthma, through collaborations with Hunninghake, Diaz, and Washko labs, as well as the Harvard T.H. Chan School of Public Health, National Jewish Health, University of Colorado, Northeastern University, and the University of Virginia.  We have access to large cohorts including the UK Biobank, TOPMed, COPDGene, ECLIPSE, and are funded by several R01 grants.

We are committed to training physician-scientists and have an excellent track record of funding success and high impact publications that help trainees to advance their careers.

Discovery: Disease mechanisms through genetics and multiple omics

Our discovery efforts include genome-wide association studies of COPD, interstitial lung abnormalities, mucus plugging, and related phenotypes through the International COPD Genetics Consortium, and through the Trans-Omics in Precision Medicine (TOPMed) consortium, and collaborations with the Tobin and Wain groups at the University of Leicester, the Hunninghake and Diaz groups at BWH, and the Manichaikul lab at UVA. We work to identify common and rare genetic risk factors using state-of-the-art methods and novel phenotypes (including imaging and machine learning). We have identified dozens of new genetic risk loci, effector genes and pathways through integrative omics, and shared risk factors between diseases.

Function: Variant to Gene

Discovery of new genetic variants requires the identification of effector genes, as well as relevant cell types and functional context. We collaborate with the Silverman, Zhou (BWH) and Wilson (BU) labs to apply integrative genomics, high throughput functional studies to identify casual variants, genes, and pathways.

Benway et al, Am J Respir Cell Mol Biol. 2021, Fig 1.

Risk Prediction and Subtyping

COPD is a heterogeneous disease, likely comprised of multiple subtypes. We have used machine learning methods to identify COPD subtypes using lung function, imaging, and omics data. We have also created polygenic risk scores for COPD, pulmonary fibrosis, and asthma, and multiomic risk scores for COPD. Our risk scores can identify high risk groups that are likely to develop these chronic lung diseases and those who will experience significant disease progression.

Systems pharmacology

In addition to identifying individuals at high risk of disease and disease progression, we are interested in identifying drug targets and drug repurposing candidates to treat high-risk individuals. Using our multiomic data, we are able to utilize certain omics for risk stratification (e.g. genetics and transcriptomics) while using proteomics and metabolomics for drug repurposing analyses. Through collaborations with the Loscalzo, Manichetti, and Halu labs, we are leveraging state-of-the-art network medicine approaches to develop treatment approaches for high risk groups. We are building a multi-disciplinary systems pharmacology program utilizing multiple analytic approaches to translate Omics findings into actionable therapeutic strategies. 

Contact: Email michaelhcholab [at] gmail [dot] com