Developing a P4 Medicine Approach to Obstructive Sleep Apnea

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ABSTRACT This program is directed at developing a new approach to OSA care based on the principles of Precision Medicine, with a focus on improving prediction, prevention and personalization. The program has 4 projects and 3 cores to support the work of the investigators. Project 01 (Genetics of Extreme Phenotypes of OSA and Associated Upper Airway Anatomy) is focused on identifying both common and rare genetic variants that are associated with risk for OSA. Since OSA has multiple pathways to disease, identifying associated genetic variants is challenging. This project investigates gene variants associated with quantitative intermediate traits for the disorder. The focus is on structural risk factors—both craniofacial dimensions and soft tissues. A focus is on tongue fat, a specific heritable distribution of fat that mediates the effect of obesity in causing OSA. Machine learning approaches have been developed to allow quantification of traits of interest from a large number of relevant clinically-obtained CT and MR images in individuals with genetic data. Data from this project will be used in combination with data from ongoing genetic studies to develop a polygenic risk score (PRS) for OSA with wide-applicability. Project 02 (MicroRNAs as Biomarkers for Obstructive Sleep Apnea) will study microRNAs as a biomarker relevant to OSA using RNA sequencing of all short microRNAs. Expression of microRNAs is dynamic; they respond to multiple challenges, including hypoxia. MicroRNAs are being used in development of biomarkers in multiple areas, with supportive data in OSA, albeit in relatively small samples. Thus, we will employ a combination of a hypothesis-driven approach complimented by a broader discovery strategy. Biomarkers will be developed to help identify cases with OSA, as well as to assess effectiveness of therapy and to provide prognostic information about who with OSA will have blood pressure reduction with treatment. Project 03 (Mechanisms that Account for Different Symptom Subtypes of OSA) will examine the physiological and multi-omics determinants of robustly validated symptom subtypes of OSA. There will be an emphasis on the excessively sleepy subtype, which has been shown to be at elevated cardiovascular risk. We will evaluate whether there are differences in physiological responses during sleep in the different subtypes and/or whether there are genetic, epigenetic, and metabolomic differences. Project 04 (Going from Genetic Associations to Identification of Causative Genes) will focus on identifying causative genes that explain GWAS associations. For genes conferring risk for OSA, we will begin with existing GWAS data complimented by data from Project 01. For sleepiness, this project will start with recently published genetic loci, and include analyses based on genes identified in Project 03. We will first use cell-based approaches to identify possible causative genes. The role of these genes will be assessed in high-diversity mouse models (for genes associated with anatomy) and in Drosophila and zebrafish (for genes associated with sleepiness).