Multi-omic network methods for mapping molecular trajectories of age-related lung diseases - Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are progressive lung diseases that are substantial sources of morbidity and mortality worldwide. Both diseases have common origins beginning with injury to the lung from environmental exposures such as cigarette smoke or particulates from biomass fuels. However, people with similar exposures have divergent outcomes: some people develop COPD, some develop IPF, and some develop neither condition. Moreover, COPD and IPF are both highly heterogeneous conditions. While genetic factors can explain a small amount of this variability, the molecular mechanisms underlying these divergent outcomes are largely not understood. We hypothesize that the diversity in COPD and IPF is a reflection of the interaction of genetic differences with environmental factors, thus explaining how similar exposures can result in such broadly varying phenotypes. We propose that these gene-environment interactions can be assessed across the genome by incorporating an epigenetic perspective into models of genetic and genomic variation in lung disease. This application will support the development and application of new multi-omic methods needed to elucidate the shared and divergent biology underlying COPD and IPF. Specifically, the new methods will 1) map conditional dependence between genotype, gene expression, and DNA methylation in a mixed graphical model framework; 2) use multi-omic networks to identify a panel of genes involved in the differential etiology of COPD and IPF; and 3) map lung disease trajectories using network embedding of multi-omic similarity networks constructed from controls, COPD cases, and IPF cases. Dr. Shutta is a biostatistician with training in mathematics, computer science, statistics, and systems biology. Her goal is to become an independent investigator conducting research into complex pulmonary diseases by developing and applying novel methods for multi-omic integration in network medicine. Dr. Shutta’s training will be conducted jointly at the Harvard School of Public Health and at the Channing Division of Network Medicine (Brigham and Women’s Hospital/Harvard Medical School). In these neighboring institutions, Dr. Shutta will have access to two complementary and rich training environments - an ideal setting to pursue the integrative work in this proposal by connecting with methods-focused biostatisticians at HSPH and clinically-focused researchers and physician-scientists at BWH/HMS. Dr. Shutta’s path to independence will be supported by experienced mentors, an advisory committee, and top-quality didactic training in respiratory pathophysiology, numerical optimization, and scientific computing. The work completed during this proposal will support Dr. Shutta’s path to independence as a biostatistician and contribute new understandings of the biology of COPD and IPF.
