Genetic Epidemiology of Sleep Apnea and Comorbidities in Biobanks

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ABSTRACT Sleep apnea (SA) and insomnia are the two most common sleep disorders, and both contribute individually and jointly to the risk of cardiopulmonary, metabolic, and psychiatric diseases. Despite their high prevalence, treatments for SA and insomnia remain suboptimal. SA and insomnia are thought to be comprised of distinct subtypes, which remain poorly defined and may contribute to differing risks for health outcomes. Our goal is to use machine learning to apply precise phenotyping to biobanks to identify the genetic bases of SA and insomnia and discover SA and insomnia subtypes based on genetics and comorbidities in order to reduce phenotype heterogeneity, guide patient stratification and aid in the discovery of more personalized treatments. Our approach is to combine health care system biobank data with research polysomnography (PSG) to achieve statistical power to discover genetic variants for SA and insomnia-related phenotypes and characterize their associated clinical outcomes and endophenotypes (physiological mechanisms). We will use advanced natural language processing (NLP) methods to substantially improve the accuracy of SA and insomnia phenotyping. Our anticipated sample size will be >11-fold larger than prior genetic studies of SA, providing the necessary statistical power for genetic discovery. Polygenic risk scores derived from our results can be used to quantify sleep disorder risk, even among those without sleep phenotypes. Machine learning methods can identify predictors of diagnosis-clustered patient groups contained within the medical record. Precision deeply- phenotyped PSG data (eg hypoxic burden) can characterize endophenotypes at associated genetic loci using genetic localization. We will derive advanced SA and insomnia phenotypes robust to demographic differences across biobank sites, perform the largest genetic analysis of validated SA and insomnia phenotypes to date, characterize novel loci, and study associations with clinical diagnosis data to improve patient classification in three biobanks. We will explore sex-specific associations and validate lead genetic associations in two biobanks. Our specific aims are: 1) to construct advanced SA and insomnia phenotying algorithms across diverse demographic groups and sites; 2) to identify and characterize the genetic associations with SA and insomnia; and 3) to identify and characterize distinct SA and insomnia patient subgroups based on related comorbidity profiles. The proposed project has a goal of improving the treatment of heart, lung, blood, and sleep disorders by potentially resolving disease heterogeneity, discovering novel genetic associations with sleep disorders, and helping to clarify the overlap of SA and insomnia with cardiopulmonary, metabolic, and psychiatric disease.