Heritabilities and genetic trends for elbow score as recorded by the New Zealand Veterinary Association Elbow Dysplasia Scheme (1992–2013) in four breeds of dog
Bibliographic record
Abstract
AIM: To estimate the heritability of the New Zealand Veterinary Association (NZVA) elbow phenotype, obtain estimated breeding values (EBV) for the worst-elbow score and estimate the genetic trends for this trait in four populous breeds of dogs, using the records from the NZVA Canine Elbow Dysplasia Scheme database (1992-2013). METHODS: Overall, 4,070 elbow records from a pedigree of 11,311 dogs were available for animals scored between 1992 and 2013. The worst elbow score between the left and right elbows was identified for each dog and used for EBV analysis. Estimates of heritability and EBV for the elbow score of dogs from German Shepherd dog, Labrador Retriever, Golden Retriever and Rottweiler breeds were obtained using restricted maximum likelihood procedures with a within-breed linear animal model. The model included the fixed effects of sex and birth year, with age at scoring as a covariable, and the random effect of animal. Genetic trends for the worst-elbow score were calculated as the regression coefficient of the EBV, weighted by reliabilities, on year of birth. RESULTS: The estimates of heritability for worst-elbow score were 0.25 (SE 0.06) in German Shepherd dogs, 0.46 (SE 0.06) in Labrador Retrievers, 0.18 (SE 0.07) in Golden Retrievers and 0.29 (SE 0.11) in Rottweilers. The genetic trend for German Shepherd dogs was -0.0082 (SE 0.0015), for Labrador Retrievers was -0.0016 (SE 0.0016), for Golden Retrievers was -0.0033 (SE 0.0010) and for Rottweilers was -0.0070 (SE 0.0023) units per annum, which were different from zero (p<0.01) in all breeds except Labrador Retrievers. CONCLUSIONS AND CLINICAL RELEVANCE: A small but favourable response to selection was achieved by three of the four breeds in the study period; during which selection for elbow traits has been largely voluntary. While the magnitude of genetic change in terms of elbow units per annum may appear small, it must be remembered that elbow scoring grades only range from 0-3. Greater improvement may be possible if compulsory screening was a requirement for pedigree breeding stock, and if greater selection pressure were applied on the basis on an individual's EBV, rather than the worst-elbow score alone. The maintenance of an open registry, with transparency of EBV information made available to all breeders, may enhance selection intensity opportunities and potentially assist with the process and progress of breeding selection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".