Evaluating the frequency and the impact of pharmacogenetic variants in an ancestrally diverse Bioban
Updated: Sep 29
Karl Keat1*, Shefali S. Verma2*, Binglan Li3, Glenda Hoffecker4, Marjorie Risman5, Regeneron Genetics Center, Katrin Sangkuhl3, Michelle Whirl-Carrillo3, Scott Dudek5, Anurag Verma4, Teri E. Klein3,6, Marylyn D. Ritchie5*, Sony Tuteja4* 1Genomics & Computational Biology PhD Program, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA 2Department of Pathology & Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA 3Department of Biomedical Data Science, Stanford University, Stanford, California, USA 4Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA 5Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA 6Department of Biomedical Data Science and Medicine (BMIR), Stanford University, Stanford, California, USA
Pharmacogenetics (PGx) aims to utilize a patient's genetic data to enable safer and more effective prescribing of medication. The Clinical Pharmacogenetics Implementation Consortium (CPIC) provides guidelines for 24 genes affecting 72 medications. Despite strong evidence linking PGx variants to drug response, there is a large gap in the actual implementation and return of actionable pharmacogenomic findings to patients in standard clinical practice. In this study, we evaluated opportunities for genetically guided medication prescribing in a diverse health system and determined the frequencies of actionable PGx alleles in an ancestrally diverse biobank population. A retrospective data mining of Penn Medicine electronic health record (EHR) data, which includes ~3.3 million patients between 2012-2020 provides a snapshot of the distribution of highly ranked CPIC drugs in the Penn Medicine health system. We identified ~316,000 unique patients that were prescribed at least 2 CPIC Level A or B drugs. Additionally, the Penn Medicine BioBank (PMBB) consists of a diverse group of 43,359 participants with linked EHR and genotypes. We used the Pharmacogenomics Clinical Annotation Tool (PharmCAT) to identify PMBB participants with actionable PGx phenotypes and measure PGx phenotype frequencies and clinical burden. All genotyped participants had at least one non-reference allele in a CPIC gene and 98% of participants are carriers of one or more PGx actionable phenotypes, indicating a modification in drug therapy would be recommended. Furthermore, 13.3% of participants (n=5785) were prescribed medications impacted by their PGx alleles. We found 849 participants who received clopidogrel with CYP2C19 intermediate or poor metabolizer phenotypes who were at increased risk for major adverse cardiovascular events. When we stratified by genetic ancestry, we found disparities in PGx allele frequencies and clinical burden. Notably, clopidogrel users of Asian ancestry had significantly higher rates of CYP2C19 intermediate or poor metabolizer phenotypes than European ancestry users (p<0.0001, OR=3.72).