Population pharmacogenomics in Russia: insights from 6102 exomes and implications for genomic medicine

Vollständiger Abstract

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Abstract Background Personalized pharmacotherapy requires systematic consideration of genetic factors influencing drug efficacy and safety. The accumulation of large-scale whole-exome sequencing (WES) resources provides an opportunity to assess population frequencies of clinically significant pharmacogenetic variants; however, the applicability of exome-based pharmacogenomics across populations, particularly those underrepresented in existing reference datasets, requires further evaluation. Materials and methods A retrospective analysis of 6102 anonymized sequencing datasets obtained between 2020 and 2025 was performed using the DNBSEQ-G400 (MGI) platform and Agilent SureSelect Human All Exon v6/v7/v8 enrichment kits. SNV and indel detection, CNV analysis, high-resolution HLA typing, and diplotype assignment for key pharmacogenes were conducted. Pharmacogenomic annotations were derived from ClinPGx (formerly PharmGKB) (levels of evidence 1 A–2B), CPIC, and PharmVar. Haplotype phasing (SHAPEIT5), genotype imputation (IMPUTE5), and phased linkage disequilibrium analysis were performed using a reference panel of 814 Russian whole-genome sequences generated with the same bioinformatic workflow to assess the feasibility of reconstructing clinically relevant non-coding pharmacogenetic variants not captured by WES. Results WES reliably detected 33 of 34 Very Important Pharmacogenes (VIPs), allowing determination of allele frequencies, metabolizer statuses for 13 VIPs, and HLA diversity. The highest allelic and phenotypic variability was observed in CYP2D6 , CYP2C19 , and CYP2B6 . A total of 663 ClinPGx annotations were identified, predominantly related to drug metabolism (50.38%) and toxicity (29.56%), involving psychotropic drugs, anticoagulants, statins, opioid analgesics, antineoplastic agents, and immunosuppressants. However, at least 32 drugs require assessment of non-coding pharmacogenetic variants or accurate CYP2D6 copy number determination – both beyond standard WES capability. Phased haplotype analysis demonstrated that haplotype-based imputation was feasible for only a limited subset of clinically relevant non-coding pharmacogenetic variants, whereas the majority lacked informative exonic proxies required for accurate inference from WES data. Conclusion These findings represent one of the largest reference resource to date of pharmacogenetically significant variant and HLA allele frequencies in the Russian population. The results confirm WES as a robust and scalable platform for population-level pharmacogenomic screening and broader clinical genomic applications, while demonstrating that haplotype-based imputation cannot fully overcome the intrinsic limitations of exome sequencing for comprehensive pharmacogenomic profiling. Selection of genomic testing strategies should balance diagnostic completeness, analytical complexity, cost, scalability, and long-term potential for reanalysis.