Unraveling IGK Locus in Dog Breeds: IMGT® New Insights into Canine Immunogenetics
Bibliographic record
Abstract
Abstract Over millennia, the selective breeding of dogs ( Canis lupus familiaris ) has generated remarkable genetic diversity among breeds, highlighting the need for comprehensive genomic and immunogenetic studies. This research provides detailed immunoglobulin kappa light chain locus (IGK) analysis across multiple dog breeds. It aims to uncover breed-specific genetic variations and their implications for immunology and veterinary medicine. The primary objectives were to do the biocuration of the IGK locus in nine canine genome assemblies, investigate structural variations, polymorphisms, and gene diversity, and to enrich the IMGT® database with comprehensive IGK data from diverse breeds, creating a more inclusive genetic resource. Our extensive annotation of breeds, including the Bernese Mountain Dog, Boxer, Cairn Terrier, Labrador Retriever, Great Dane, Basenji, and German Shepherd, identified 40 genes and 97 alleles, revealing both conserved genes and unique variants across these breeds, with in silico validation through Sanger sequencing. Notably, we analyzed discrepancies in the first reference assembly from the Boxer breed (Canfam3.1), highlighting potential errors in assembly, challenges in gene and allele nomenclature, and a low-density region within the canine IGK locus. This study not only refines the understanding of IGK locus diversity but also contributes to the IMGT® databases, advancing future research on immunogenetic variability, somatic mutations, and immune response dynamics in canine health and disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".