Whole Genomic Characterization of Sleep Apnea Traits and Comorbid Disorders

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Project Summary (Abstract). Sleep apnea (SA) is a common disorder, associated with increased mortality and multiple adverse health outcomes. Yet major aspects of the genetic architecture of SA remain largely unknown. Knowledge of genes that influence physiologically relevant SA phenotypes can provide an important avenue for the development of therapeutic interventions. Decreasing costs and increasing genetic resolution continue to raise the attractiveness of genetic analyses to aid in discovery of the molecular mechanisms of complex disorders such as SA. I have played a central role in the genetic analysis of SA with the assembly and harmonization of the largest genomic dataset of individuals with objective sleep phenotyping in the world. This has led to the discovery of several genome-level significant associations with SA traits, which include loci in biologically compelling pathways. Further advances require analysis of larger datasets with higher resolution genetic data, permitting detailed characterization of rare and functional variants, as well as strategic follow-up physiological studies to elucidate mechanistic pathways. An unprecedented opportunity to conduct this research has emerged through genetic data linked to electronic health records (EHR) in over 150,000 individuals and the establishment of the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium. Maximal genetic resolution will be provided through whole-genome sequencing of 1,000 members of the Cleveland Family Study, and >3,000 additional unrelated individuals in cohorts with polysomnography. To discover causal genetic variants for SA, I will a) perform a GWAS using an EHR-based case-control design; b) use these associations and 88 other associations we recently identified to interrogate causal variants for SA using TOPMed whole-genome sequencing data from well-phenotyped cohorts; c) apply multivariate analyses that exploit the shared genetic architecture for SA with cardiometabolic traits to increase statistical power and elucidate shared biology. Highly heritable, physiologically relevant SA traits will be used to discover additional associated loci that are not evident using the clinical Apnea Hypopnea Index (AHI) measure. Advanced statistics that integrate association analyses, linkage results from families enriched with rare variants, regulatory regions, and protein mutation severity will maximize power for common and rare variant association fine-mapping. The prominent NIH commitment to large-scale studies such as TOPMed and the Precision Medicine Initiative demonstrates a promising and sustained future for the field of genetic epidemiology and a need for suitably trained investigators. The proposed genomic analysis of SA is further responsive by addressing multiple goals of the NIH Sleep Disorders Research Plan. The proposed work and developed skills will position me as an independent researcher capable of further identifying the role of genetic variants in the etiology and co-morbidity of sleep apnea.