Optimisation of breeding strategies to reduce the prevalence of inherited disease in pedigree dogs
Bibliographic record
Abstract
Abstract One option for improving the welfare of purebred dog breeds is to implement health breeding programmes, which allow selection to be directed against known diseases while controlling the rate of inbreeding to a minimal level in order to maintain the long-term health of the breed. The aim of this study is to evaluate the predicted impact of selection against disease in two breeds: the Cavalier King Charles spaniel (CKCS) and the Labrador Retriever. Heritabilities for mitral valve disease, syringomyelia in the CKCS and hip dysplasia in the Labrador were estimated to be 0.64 (± 0.07), 0.32 (± 0.125) and 0.35 (± 0.016), respectively, which suggest encouraging selection responses are feasible based upon the estimation of breeding values (EBVs) if monitoring schemes are maintained for these breeds. Although using data from disease databases can introduce problems due to bias, as a result of individuals and families with disease usually being over-represented, the data presented is a step forward in providing information on risk. EBVs will allow breeders to distinguish between potential parents of high and low risk, after removing the influence of life history events. Analysis of current population structure, including numbers of dogs used for breeding, average kinship and average inbreeding provides a basis from which to compare breeding strategies. Predictions can then be made about the number of generations it will take to eradicate disease, the number of affected individuals that will be born during the course of selective breeding and the benefits that can be obtained by using optimisation to constrain inbreeding to a pre-defined sustainable rate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".