Friday, May 9, 2025
5/9/2025
EpiGNOME: Epigenetic Graphical Networks Of Methylation Effectors for COPD Progression - EpiGNOME: Epigenetic Graphical Networks Of Methylation Effectors for COPD Progression ABSTRACT Modern clinical and translational sciences rely on big data to identify drivers of disease progression and develop predictors of disease outcomes. Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death and likely represents several molecular subtypes, which makes the prognosis and treatment challenging. Genetic variation only explains a fraction of COPD pathogenesis. DNA methylation variability is particularly important since it is more stable than gene expression and captures genetic influences and longitudinal exposures. Despite its likely prominent role in disease and potential for therapeutic targeting, limited investigation has focused on methylation, partly because of the analytic challenges posed by the high-dimensional and collinear data. This project focuses on the investigation of the role of DNA methylation in COPD progression. We will develop novel directed (causal) graph methods for high dimensional collinear data. These methods will provide a foundation for advancing integrative computational systems biology by overcoming challenges inherent in large scale methylation datasets. Through these new methods, applied at a population scale, we will identify potential cause- effect relationships among DNA methylation and genetic and clinical effectors related to COPD subtypes and progression. Our premise is that DNA methylation will reveal molecular mechanisms and drive the development of stable COPD disease subtypes, not previously revealed with genetic and gene expression analyses. NHLBI's Trans-Omics for Precision MEDicine (TOPMED) initiative includes a massive multi-Omic profiling program that promises to break down barriers to understanding lung diseases. We will assemble results of genome-wide DNA methylation analysis from leukocytes (two cohorts: COPDGene, LTRC), and lung tissue biopsies (two cohorts: LTRC, LTCOPD); and longitudinal DNA methylation data from over 11,000 samples. These resources provide the foundation for the development of a novel computational framework for DNA methylation analysis and will enable us to further investigate COPD subtypes and identify effectors of COPD progression. The logic of our approach begins with the identification of methylation marks directly linked to COPD outcomes from cross- sectional leukocyte DNA methylation (Aim 1) and proceeds with similar analyses using repeated measures of DNA methylation and pulmonary phenotypes (Aim 2). Finally, in Aim 3 we will identify COPD progression subtypes and compare lung to blood epigenetic pathways. This project will advance algorithmic development beyond standard methods; it will lead to generalizable insights into features of the epigenome that may drive lung function decline and COPD progression; and identify stable COPD subtypes that will reveal new molecular mechanisms and can be used to design new disease prevention and management strategies. This proposal is a response to NOSI (NOT-HL-23-067), “Integrative Omics Analysis of NHLBI TOPMed Data”, with the bulk of discovery and methods innovation leveraging extant TOPMed data.
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