| Title: | Genetic and multi-omic basis of Symptom Subtypes of Obstructive Sleep Apnea |
| Lead Institution: | University of Pennsylvania |
| Principal investigator: |
Application 905103
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About
Obstructive sleep apnea (OSA) is a common, heritable, and serious medical condition associated with a wide range of adverse health consequences. OSA presents with heterogeneous symptoms that form subtypes characterized by degrees of excessive sleepiness, disturbed sleep, and a lack of traditional symptoms. 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 symptomatic subgroup. This proposal builds on the established relevance of OSA symptom subtypes and seeks to expand knowledge by understanding the genetic architecture and multi-omics signatures associated with OSA subtypes, with a particular focus on excessive sleepiness. Our first objective is to use existing UK Biobank data to examine the underlying common and rare genetic variation associated with OSA symptom subtypes and a quantitative measure of excessive sleepiness (the Sleep Consequences Questionnaire). Prior studies and our preliminary data have identified several genetic risk loci for OSA and sleepiness, and suggest a relationship between changes in methylomic, transcriptomic, and metabolomic profiles and OSA symptoms. Our second objective is to apply integrative methods to combine sequencing data with multi-omics data from UK Biobank to identify potential causal genes and biological pathways through which genetic risk loci influence OSA symptom subtypes and related traits. When combined with results from other large-scale resources (e.g., the NHLBI Trans-Omics for Precision Medicine [TOPMed] Program), these efforts will enable an efficient and cost-effective approach to identify predictors and biological mechanisms of OSA symptom subtypes and excessive sleepiness. Ultimately, results will link genetic variation to biological mechanisms and physiological indicators, improving understanding and enabling more personalized interventions.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 19915 | Phenotype Refinement Influences GWAS-Implicated Variant Effect Sizes for Insomnia | Amber J. Zimmerman (+7) | 2026 | Journal of Sleep Research |
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