| Summary: | Abstract Background Obstructive sleep apnea (OSA) is defined by frequent episodes of reduced or complete cessation of airflow during sleep and is linked to negative health outcomes. Understanding the genetic factors influencing expression of OSA may lead to new treatment strategies. Electronic health records (EHRs) can be leveraged to both validate previously reported OSA-associated genomic variation and detect novel relationships between these variants and comorbidities. Methods We identified candidate single nucleotide polymorphisms (SNPs) via systematic literature review of existing research. Using datasets available at Geisinger (n = 39,407) and Vanderbilt University Medical Center (n = 24,084), we evaluated associations between 40 previously implicated SNPs and OSA diagnosis, defined using clinical codes. We also evaluated associations between these SNPs and OSA severity measures obtained from sleep reports at Geisinger (n = 6571). Finally, we used a phenome-wide association study approach to help reveal pleiotropic genetic effects between OSA candidate SNPs and other clinical codes and laboratory values available in the EHR. Results Most previously reported OSA candidate SNPs showed minimal to no evidence for associations with OSA diagnosis or severity in the EHR-derived datasets. Three SNPs in LEPR, MMP-9, and GABBR1 validated for an association with OSA diagnosis in European Americans; the SNP in GABBR1 was associated following meta-analysis of results from both clinical populations. The GABBR1 and LEPR SNPs, and one additional SNP, were associated with OSA severity measures in European Americans from Geisinger. Three additional candidate OSA SNPs were not associated with OSA-related traits but instead with hyperlipidemia and autoimmune diseases of the thyroid. Conclusions To our knowledge, this is one of the largest candidate gene studies and one of the first phenome-wide association studies of OSA genomic variation. Results validate genetic associates with OSA in the LEPR, MMP-9 and GABBR1 genes, but suggest that the majority of previously identified genetic associations with OSA may be false positives. Phenome-wide analyses provide evidence of mediated pleiotropy. Future well-powered genome-wide association analyses of OSA risk and severity across populations with diverse ancestral backgrounds are needed. The comprehensive nature of the analyses represents a platform for informing future work focused on understanding how genetic data can be useful to informing treatment of OSA and related comorbidities.
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