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ORIGINAL RESEARCH article
Front. Pharmacol.
Sec. Pharmacogenetics and Pharmacogenomics
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1553536
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Pharmacogenomics offers a possibility of anticipating drug response based on individuals’ genetic profiles and represents a step toward implementation of personalized treatment through routine genetic testing. Development of high-throughput sequencing technologies aided identification and interpretation of variants in many pharmacogenes simultaneously. Nonetheless, the integration of pharmacogenomics into clinical practice is arduous, partly due to insufficient knowledge of ethnic pharmacogenetic data. The aim of our study was to assemble the most comprehensive pharmacogenomics landscape of the Serbian population so far. We used genomic data of 881 individuals from Serbia obtained by clinical and whole exome sequencing. Raw sequencing files were processed using an in-house pipeline for alignment and variant calling. For annotation of pharmacogenetics star alleles and determination of phenotypes, we used the PharmCAT and Stargazer tools. Star allele and phenotype frequencies were calculated and compared to worldwide and European populations. Population differentiation was presented through calculation of Wright’s fixation index. Our results showed that population differentiation was the highest between the Serbian and the worldwide population. In the Serbian population, the most relevant pharmacogenes in terms of star allele frequencies and actionable phenotypes were CYP2B6, NAT2, SLCO1B1, UGT1A1 and VKORC1, that had significantly different distribution compared to other European populations. In conclusion, significant differences in frequencies of pharmacogenetic phenotypes that influence response to several drug categories including statins and antidepressants indicate that inclusion of data relevant for drug response to genetic reports would be beneficial in the Serbian population. Implementation of pharmacogenetic testing could be achieved through analysis of clinical and whole exome sequencing data.
Keywords: pharmacogenomics, bioinformatics, Population Genetics, personalized medicine, high-throughput sequencing
Received: 30 Dec 2024; Accepted: 18 Feb 2025.
Copyright: © 2025 Jelovac, Pavlovic, Stankovic, Kotur, Ristivojević, Pavlovic and Zukic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Branka Zukic, Group for Molecular Biomedicine, Department of Human Molecular Genetics and Genomics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, 11010, Serbia
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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