ABSTRACT Obstructive sleep apnea (OSA) is a common, heritable, and serious medical condition associated with a wide range of adverse health consequences. OSA presents with a heterogeneous set of symptoms that form subtypes characterized by degrees of excessive sleepiness, disturbed sleep, and a lack of traditional symptoms. Further, downstream disease risk differs by symptom subtypes. For example, increased cardiovascular risk conferred by OSA is driven by patients in the excessively sleepy subtype, highlighting the importance of this symptom. This Project builds off the established existence and relevance of OSA symptom subtypes, and seeks to expand knowledge by understanding the physiology, genetic architecture and multi-Omics signatures associated with OSA symptom subtypes, with a particular focus on excessive sleepiness. Aim 1 leverages traditional and novel electroencephalographic (EEG)-based metrics to assess whether differences in the physiologic response to OSA explain different symptom manifestations. Prior data suggest patients in different subtypes have similar apnea- hypopnea index (AHI), but there has not been a detailed examination of other physiological traits. Aim 2 uses existing data resources to examine the underlying common and rare frequency genetic variation associated with OSA symptom subtypes, a quantitative measure of excessive sleepiness, and physiological features of OSA symptom subtypes. Previous studies and our own preliminary data have identified several loci genetic risk loci for OSA and sleepiness, and highlight a possible relationship of changes in methylomic, transcriptomic, and metabolomic profiles with OSA and related symptoms. Thus, Aim 3 will apply integrative methods to combine sequencing data with multi-Omics data from NHLBI's TOPMed Program to identify potential causal genes and biological pathways through which genetic risk loci operate to affect OSA symptom subtypes and risk factors. We propose an efficient and cost-effective approach to identify physiological, genetic and other Omic predictors of OSA symptom subtypes and excessive sleepiness. Ultimately, results will move the field forward by connecting genetic variation to biological mechanisms and physiological indicators, thus improving our scientific understanding while identifying new predictors and biological targets for early and precise interventions. The National Center on Sleep Disorders Research (NCSDR) has recently identified high priority critical opportunities to help reach its strategic goals, including identifying biomarkers for sleep disorders and using Omics approaches to advance precision medicine. This Project speaks directly to these opportunities.