Evaluating the frequency and the impact of pharmacogenetic alleles in an ancestrally diverse Biobank population

Abstract

BackgroundPharmacogenomics (PGx) aims to utilize a patient’s genetic data to enable safer and more effective prescribing of medications. The Clinical Pharmacogenetics Implementation Consortium (CPIC) provides guidelines with strong evidence for 24 genes that affect 72 medications. Despite strong evidence linking PGx alleles to drug response, there is a large gap in the implementation and return of actionable pharmacogenetic 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.MethodsA retrospective analysis of the Penn Medicine electronic health records (EHRs), which includes ∼3.3 million patients between 2012-2020, provides a snapshot of the trends in prescriptions for drugs with genotype-based prescribing guidelines (‘CPIC level A or B’) in the Penn Medicine health system. The Penn Medicine BioBank (PMBB) consists of a diverse group of 43,359 participants whose EHRs are linked to genome-wide SNP array and whole exome sequencing (WES) data. We used the Pharmacogenomics Clinical Annotation Tool (PharmCAT), to annotate PGx alleles from PMBB variant call format (VCF) files and identify samples with actionable PGx alleles.ResultsWe identified ∼316,000 unique patients that were prescribed at least 2 drugs with CPIC Level A or B guidelines. Genetic analysis in PMBB identified that 98.9% of participants carry one or more PGx actionable alleles where treatment modification would be recommended. After linking the genetic data with prescription data from the EHR, 14.2% of participants (n=6157) were prescribed medications that could be impacted by their genotype (as indicated by their PharmCAT report). For example, 856 participants received clopidogrel who carried CYP2C19 reduced function alleles, placing them at increased risk for major adverse cardiovascular events. When we stratified by genetic ancestry, we found disparities in PGx allele frequencies and clinical burden. Clopidogrel users of Asian ancestry in PMBB had significantly higher rates of CYP2C19 actionable alleles than European ancestry users of clopidrogrel (p<0.0001, OR=3.68).ConclusionsClinically actionable PGx alleles are highly prevalent in our health system and many patients were prescribed medications that could be affected by PGx alleles. These results illustrate the potential utility of preemptive genotyping for tailoring of medications and implementation of PGx into routine clinical care.

How to Run the Pharmacogenomics Clinical Annotation Tool (PharmCAT)

“…In the TSV output, each row is a sample-gene pair with sample ID, gene name, PGx annotations, and missing alleledefining positions if applicable. The commands are as follows: You can find these utility R scripts and also another example, which is applied to the Penn Medicine BioBank data 22 and is about how to convert JSON data into a CSV file using python, on the PharmCAT website (https://pharm cat.org/using/ Multi -Sampl e-Analy sis/).…”

“…You can find these utility R scripts and also another example, which is applied to the Penn Medicine BioBank data 22 and is about how to convert JSON data into a CSV file using python, on the PharmCAT website (https://pharmcat.org/using/Multi-Sample-Analysis/).…”