2025/11/06 by Marc Gros La Faige, Emmanuelle Génin, Anthony Herzig · 1 voice
Biochemistry, Genetics and Molecular Biology · Pharmacology, Toxicology and Pharmaceutics · #Genetic Associations and Epidemiology #Genomics and Rare Diseases #Pharmacogenetics and Drug Metabolism
paper · pdf · doi:10.21203/rs.3.rs-7924414/v1
crossref issued 2025/11/06 · crossref published 2025/11/06 · openalex publication_date 2025/11/06 · openalex created_date 2025/11/06 · crossref created 2025/11/06 · crossref deposited 2026/06/18 · crossref indexed 2026/06/18 · openalex updated_date 2026/07/22
Abstract Genetic polymorphisms are common in pharmacogenes, with sometimes important implications for drug metabolism. Assessing the correct enzyme phenotype from genetic data is thus a crucial step into the development of personalized medicine. Many bioinformatics tools have been developed for this purpose, each of them having their specific method and limitations. Given that little is known about their performances depending on various parameters, we performed a general comparison of four star-allele callers: PyPGx, ursaPGx, PharmCAT and Aldy. We found that PyPGx and Aldy are overall more performant than the others. Moreover, using imputation or low-pass sequencing data can enhance the accuracy of star-allele callers compared to SNP-chip genotyping data only. Finally, we noticed that the concordance between star-allele callers is highly dependent on population ancestry. Our study provides new recommendations about the algorithm clinicians and researchers should use regarding the pharmacogene and the type of data they have access to.