Prevalence of the SOD1, PRCD and SLC2A9 gene mutations responsible for degenerative myelopathy, progressive rod-cone degeneration, and hyperuricosuria in Polish population of Labrador Retriever dogs
Bibliographic record
Abstract
Knowledge of the molecular background of hereditary diseases facilitates the unambiguous diagnosis of affected animals and the identification of healthy carriers, which is particularly important from a breeding perspective. To date 330 canine diseases with at least one known causative variant have been described. Degenerative myelopathy (DM), caused by a mutation in the SOD1 gene; progressive rod-cone degeneration (PRCD), caused by a mutation in the PRCD gene; and hyperuricosuria (HUU), caused by a mutation in the SLC2A9 gene, are among the most common monogenic autosomal recessive diseases identified in numerous dog breeds; however, their incidence varies significantly among breeds. The Labrador Retriever is a popular breed in Poland, and it was assumed that the known causative DNA variants for these three diseases are also present in its gene pool. The aim of this study was to analyze the distribution of these causal mutations in the Polish population of this breed. In total, 200 dogs were studied using Sanger sequencing. Among them, 32 carriers (16%) and 4 affected individuals (2%) were identified for PRCD, and 2 carriers (1%) were identified for HUU, while all studied dogs were free of the SOD1 mutation. The results obtained were compared with data for over 16,800 Labrador Retrievers published by Donner et al. (2023). We concluded that the frequency of the causal mutation responsible for DM in the Polish population is lower, while the frequencies of the causative variants for PRCD (0.01) and HUU (0.005) are slightly higher.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".