Genetic and Transcriptomic Resilience in Chronic Obstructive Pulmonary Disease - PROJECT SUMMARY I am an academic clinician scientist with a focus on the genetics of respiratory diseases. My overarching career goal is to make an important contribution to the public health burden of chronic respiratory diseases. I aim to become an independent investigator with the skill set to utilize integrative ‘omic techniques for the improved biological understanding and clinical management of chronic obstructive pulmonary disease (COPD). The proposed research combines my prior experience in genetics and transcriptomics with training in genetic association, transcriptomic analyses, and machine learning, in order to better understand the variability in COPD genetic risk. The hypothesis of this proposal is that there are genetic variants and transcriptomic factors that are associated with resilience to COPD. This will be tested by leveraging existing data from multiple well-phenotyped studies from the NHLBI TransOmics for Precision Medicine (TOPMed) program. Specific Aims: (1) Identify genetic variation associated with resilience to COPD against a background of elevated genetic risk; (2) Define clinical, imaging, and blood gene-expression features of COPD resilience; and (3) Predict lung-tissue gene-expression profiles of COPD risk and resilience from blood gene-expression data. The cutting-edge research plan utilizes innovative methodologies to characterize genetic and transcriptomic resilience to COPD. It is accompanied by a training plan that will provide me with the skills to complete the research aims and the experience to transition to an independent research career. I plan to submit an R01 expanding upon the characterization of genetic and transcriptomic resilience in COPD by combining multi-omic risk- and resilience-scoring and lung-tissue gene-expression imputation to improve understanding of the biological mechanisms that contribute to observed clinical heterogeneity in COPD. In particular, I have four training goals that build upon my existing background in respiratory genetics. (1) Strengthen my knowledge of biostatistics, statistical genomics, and bioinformatic methods; (2) Expand my skills in transcriptomic methodologies; (3) Gain experience and expertise in the development and implementation of machine-learning algorithms; and (4) Further develop my mentoring skills and understanding of study design. I am supported by a mentoring team with complementary skillsets and successful mentoring careers, which, together with my experience, training, and substantial preliminary data, will guarantee the success of this proposal. The findings may pave the way for the development of precision risk-prediction approaches, while implementing a novel methodology to address one of the most important challenges in the field today: identification of genetic and transcriptomic resilience to COPD.
