HLA system nomenclature and discovery of novel allelic variants in the Kazakh population
Bibliographic record
Abstract
The human leukocyte antigen (HLA) system is among the most genetically diverse in humans, encompassing over 220 genes that encode immune proteins essential for transplant compatibility, immune regulation, and disease susceptibility. This review outlines the fundamentals of HLA nomenclature, standardized by the World Health Organization (WHO) and curated in the IPD-IMGT/HLA database. We describe the gradual improvements in HLA typing methods, ranging from serological assays to molecular-based techniques, including Sanger sequencing and next-generation sequencing (NGS), as well as interpretation software, and evaluate their strengths and limitations in allele discovery. We discuss allelic variants of HLA genes, methods for sequencing HLA alleles, and their variants. Additionally, we report the identification of four novel HLA alleles in the Kazakh population: DQB1*03:82, C*06:256, B*13:150, and A*32:95. All four alleles feature non-synonymous substitutions within peptide-binding domains, suggesting potential immunological relevance. Comparative analysis reveals that NGS enhances allele detection efficiency by 2.8-fold compared to Sanger sequencing (one novel allele per 635 typings vs. 1,773). These findings demonstrate the significant HLA diversity present in Central Asian populations, which remain underrepresented in global databases. The identification of population-specific alleles reveals critical gaps in international donor databases, underscoring the urgent need to expand HLA profiling in ethnically diverse regions to improve transplant outcomes and advance personalized immunotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".