Jelovac Marina, Pavlovic Djordje, Stankovic Biljana, Kotur Nikola, Ristivojevic Bojan, Pavlovic Sonja, Zukic Branka
Group for Molecular Biomedicine, Department of Human Molecular Genetics and Genomics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia.
Front Pharmacol. 2025 Mar 17;16:1553536. doi: 10.3389/fphar.2025.1553536. eCollection 2025.
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 highthroughput 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 and , 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.
药物基因组学提供了基于个体基因图谱预测药物反应的可能性，代表了通过常规基因检测实现个性化治疗的一个步骤。高通量测序技术的发展有助于同时识别和解释许多药物代谢基因中的变异。然而，将药物基因组学整合到临床实践中是艰巨的，部分原因是对种族药物遗传学数据的了解不足。我们研究的目的是迄今为止汇编塞尔维亚人群最全面的药物基因组学概况。
我们使用了通过临床和全外显子组测序获得的881名塞尔维亚人的基因组数据。原始测序文件使用内部流程进行比对和变异检测。为了注释药物遗传学星号等位基因并确定表型，我们使用了PharmCAT和Stargazer工具。计算星号等位基因和表型频率，并与全球和欧洲人群进行比较。通过计算赖特固定指数来呈现群体分化。
我们的结果表明，塞尔维亚人群与全球人群之间的群体分化最大。在塞尔维亚人群中，就星号等位基因频率和可操作表型而言，最相关的药物代谢基因是[具体基因1]和[具体基因2]，与其他欧洲人群相比，它们的分布有显著差异。
总之，影响对包括他汀类药物和抗抑郁药在内的几类药物反应的药物遗传学表型频率存在显著差异，这表明在塞尔维亚人群中，将与药物反应相关的数据纳入基因报告将是有益的。通过分析临床和全外显子组测序数据可以实现药物遗传学检测。
