Exploring the Use of Electronic Health Record-Linked Biorepositories for Pharmacogenomic Application and Discovery
Drug response is well documented to vary considerably among patient groups and populations, as well as within individual patients. Since drug prescribing is often based on population averages of drug response, many patients will not respond, and up to one-third may experience harmful toxicity. Genetics plays a large role in explaining the variability observed in response to different drugs and is an important factor driving precision medicine initiatives. Pharmacogenetic information can be useful in optimizing patient therapy, potentially reducing the cost of hospitalizations and treatment of adverse drug events.
As part of the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH), we analyzed 102,979 members of the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort with genetic information available, along with almost two decades of electronic health record (EHR) data, prescription records, and lifestyle survey results. In one of the largest, most ethnically diverse pharmacogene characterization studies to date, we assessed cohort metabolizer status phenotypes for 7 drug-gene interactions (DGIs) for which there is moderate to strong evidence suggesting the use of pharmacogenetic information to guide therapy. 89% of the cohort had at least one actionable allele for the 7 DGIs in this study, and we observed large variations among ethnicities. Additionally, 17,747 individuals had been prescribed a drug for which they had an actionable or high-risk metabolizer status phenotype. For these individuals, the availability of pharmacogenetic information at point-of-care may have potentially led to a more personalized drug or dosing regimen. Following this study, we assessed the utility of this resource for deriving two drug response phenotypes: weight gain induced by atypical antipsychotic use and major adverse cardiovascular events in clopiodgrel non-responders. Despite challenges in deriving phenotypes from the EHR, we were able to extract phenotypes that reflected observed estimates from previously published studies. Using these phenotypes, we performed candidate gene and genome-wide association studies to identify genetic variants associated with response. Altogether, this dissertation demonstrates the potential utility and clinical impact of integrating genetic data with EHRs for pharmacogenetic application and discovery, and provides the foundation for future studies in precision medicine.