The first Russian human genome standard E701 was developed through a collaborative research involving four laboratories: Pirogov Russian National Research Medical University, National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I.Kulakov, National Research Center Kurchatov Institute, and National Medical Research Radiological Centre. Whole-genome sequencing of short reads on various platforms (MGI Tech, Illumina) and alignment to the reference human genome GRCh38.p14 were performed for the E701 sample. Subsequently, 3842 877 genomic variants were identified, which can be used as a standard for calculating statistical quality metrics while analyzing sequencing data. This variant set was additionally filtered by the GIAB high-confidence regions version 4.2.1, yielding 3262 154 variants. Moreover, E701 sample has undergone multiple whole-exome sequencing using five different enrichment panels for exome–genome concordance analysis. Furthermore, 9096 bi-allelic variants were identified on the autosomes and the X chromosome, with a minor allele frequency exceeding 0.4. Additionally, mitochondrial DNA sequencing was performed with the breadth of coverage over 99.9% at 1000×.

IgA nephropathy (IgAN) is an autoimmune disorder which is believed to be non-monogenic. We performed an exome-wide association study of 70 children with IgAN and 637 healthy donors. The HLA allele frequencies were compared between the patients and healthy donors from the bone marrow registry of the Pirogov University. We tested 78,020 gene markers for association and performed functional enrichment analysis and transcription factor binding preference detection. We identified 333 genetic variants, employing three inheritance models. The most significant association with the disorder was observed for rs143409664 (PRAG1) in the case of the additive and dominant models (P_BONF = 1.808 × 10⁻¹⁵ and P_BONF = 1.654 × 10⁻¹⁵, respectively), and for rs13028230 (UBR3) in the case of the recessive model (P_BONF = 1.545 × 10⁻⁹). Enrichment analysis indicated the strongly overrepresented “immune system” and “kidney development” terms. The HLA-DQA1*01:01:01G allele (p = 0.0076; OR, 2.021 [95% CI, 1.322–3.048]) was significantly the most frequent among IgAN patients. Here, we characterized, for the first time, the genetic background of Russian IgAN patients, identifying the risk alleles typical of the population. The most important signals were detected in previously undescribed loci.

The first Russian human genome standard E701 was developed through a collaborative research involving four laboratories: Pirogov Russian National Research Medical University, National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I.Kulakov, National Research Center Kurchatov Institute, and National Medical Research Radiological Centre. Whole-genome sequencing of short reads on various platforms (MGI Tech, Illumina) and alignment to the reference human genome GRCh38.p14 were performed for the E701 sample. Subsequently, 3842877 high confidence genomic variants were identified, which can be used as a standard for calculating statistical quality metrics while analyzing sequencing data. Furthermore, 9096 biallelic variants were identified on the autosomes and the X chromosome, with a minor allele frequency exceeding 0.4. Additionally, mitochondrial DNA sequencing was performed with the breadth of coverage over 99.9% at 1000X.

Whole exome sequencing (WES) is essential for identifying genetic variants linked to diseases. This study compares available to date four exome enrichment kits: Agilent SureSelect Human All Exon v8, Roche KAPA HyperExome Probes, Vazyme VAHTS Target Capture Core Exome Panel, and Nanodigmbio NEXome Plus Panel v1. We evaluated target design, coverage statistics, and variant calling accuracy across these four different exome capture products. All kits showed high coverage completeness, with mean x10 coverage exceeding 97.5% and x20 coverage above 95%. Roche exhibited the most uniform coverage, indicated by the lowest fold-80 scores, while Nanodigmbio had more on-target reads due to fewer off-target reads. Variant calling performance, evaluated using in-lab standard E701 DNA sample, showed high recall rates for all kits, especially Agilent v8. All kits achieved an F-measure above 95.87%. Nanodigmbio had the highest precision with the fewest false positives but a slightly lower F-measure than other kits. This study also highlights the performance of new Chinese solutions from Vazyme and Nanodigmbio, which were comparable to Agilent v8 and Roche KAPA kits. These findings assist researchers and clinicians in selecting appropriate exome capture solutions.

IgA nephropathy (IgAN) is an autoimmune disorder that commonly manifests during adolescence. This disease is believed to be a non-monogenic disorder related to variations in multiple genes involved in various biological pathways. We performed the exome-wide association study of 70 children with IgAN confirmed by renal biopsy and 637 healthy donors to identify gene associations responsible for the disease. The HLA allele frequencies between the patients and healthy donors from the bone marrow registry of the Pirogov University were compared. We tested 78,020 gene markers for association, performed the functional enrichment analysis and the transcription factor binding preference detection. We detected 333 genetic variants, employing three inheritance models. The most significant association with the disorder was observed for rs143409664 ( PRAG1 ) in case of the additive and dominant models (P _BONF = 1.808 × 10 ⁻¹⁵ and P _BONF = 1.654 × 10 ⁻¹⁵ , respectively) and for rs13028230 ( UBR3 ) in case of the recessive model (P _BONF = 1.545 × 10 ⁻⁹ ). Enrichment analysis indicated the strongly overrepresented “immune system” and “kidney development” terms. The HLA-DQA1*01:01:01G allele (P = 0.0076; OR, 2.021 [95% CI, 1.322-3.048]) was significantly the most frequent among IgAN patients. Here we characterized, for the first time, the genetic background of the Russian IgAN patients identifying the risk alleles typical of the population. The most prominent signals were detected in previously undescribed loci.

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.