Statistical physics and network-based approaches for elucidating molecular biomarkers of COPD

PROJECT SUMMARY Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. As a common complex disease, COPD has high global morbidity and mortality. Indeed, deaths due to respiratory disease numbered nearly four million, which was mostly contributed by COPD. There is a clear demand to improve our understanding of COPD pathogenesis and develop interventions to prevent and treat COPD. Yet, a complex disease phenotype is usually determined by various pathobiological processes that interact in a network, rather than induced by the abnormality in a single effector gene product. Extensive evidence implies that disease-associated proteins have distinct interactions within the human protein-protein interaction (PPI) network (a.k.a. the human interactome), and the pathobiological processes of a complex disease are associated with perturbation within specific disease neighborhoods of the interactome, often referred to as the disease module. Comprehensive understanding of the COPD pathogenesis and predicting disease genes to inform therapeutic treatment require advanced tools to identify its disease module. Although many disease module detection methods have been reported in the literature, they all have fundamental limitations. More importantly, existing methods do not fully leverage the advantage of multi-omics data. In this application, a statistical physics and network-based framework will be developed to detect disease modules for complex human diseases using multi-omics data. This framework will be systematically validated with synthetic data. Then it will be applied to the rich multi-omics data (SNP genotyping, DNA methylation, mRNA and miRNA expression) in two large COPD cohorts. Dr. Wang’s training in statistical physics, network science and deep learning have prepared him well for his proposed research. However, understanding and interpreting the molecular basis of complex diseases and the statistical analysis of multi-omics data are still arduous tasks that will require further training in specific areas. Dr. Wang will leverage the excellent intellectual environment of Harvard Medical School and its teaching hospitals and will have access to extensive computational resources through the Channing Division of Network Medicine and Harvard Medical School. Through the guidance of a mentoring and advisory team with complementary expertise, together with formal coursework and workshops, Dr. Wang will immerse himself in a training program focusing on statistical genetics, epigenetics, multi-omics integration, and the biology of pulmonary diseases. Dr. Wang will also participate in regular meetings with his mentors and advisory committee members, allowing him to share his progress and receive timely feedback. Altogether, Dr. Wang’s training and research plan will enable him to expand his current skillset to include the ability to address the challenges of analyzing the complex genomic and epigenomic data of large epidemiological cohorts, identify open questions in the systems biology of COPD, and ultimately contribute to the precision medicine of lung diseases.