Speakers: Olivia Veatch, PhD
Olivia Veatch, PhD
Department of Psychiatry and Behavioral Sciences
University of Kansas Medical Center
Dr. Olivia Veatch is an assistant professor in the department of Psychiatry and Behavioral Sciences at the University of Kansas Medical Center. She earned her M.S. in Molecular Biosciences and Bioengineering from the University of Hawai’i in Honolulu, HI in 2006 and her Ph.D. in Human Genetics from Vanderbilt University in Nashville, TN in 2013. She also has professional training in translational bioinformatics and received a Certificate in Biomedical Informatics at the University of Pennsylvania in 2018. Dr. Veatch’s research focuses on combining evidence from the laboratory with the power of computers to find ways that genetics data can help inform healthcare.
Using Electronic Health Records to Characterize Heterogeneity in Complex Diseases
Biorepositories linked to electronic health records (EHRs) offer an unprecedented opportunity to analyze detailed phenotypic and genetic data in large cohorts of patients with complex conditions. When EHR-derived data are properly validated and curated, a wide breadth of real-world clinical information can be liberated for research. This can help establish robust genetic associations and reveal pleiotropic effects by testing variants for associations with numerous phenotypes. These endeavors could help inform more effective treatment options by providing knowledge of how convergent mechanisms influence risk for multiple disorders in the same individual and establishing biological connections across seemingly distinct conditions. This presentation will focus on best practices for conducting research with EHRs linked to genomic data using the most prevalent and complex sleep disorder in the world, obstructive sleep apnea (OSA), as proof-of-concept. We evaluated associations between previously implicated variants and OSA diagnosis/severity in datasets from Geisinger (n=39,407) and Vanderbilt University Medical Center (n=24,084). We used phenome-wide association studies to investigate pleiotropy and determine whether previous evidence of associations with OSA may reflect relationships with underlying comorbidities. The comprehensive nature of the analyses should inform future work focused on understanding how genetic data can help improve treatment of OSA and related comorbidities.