Quantitative genetics of traits associated with hip dysplasia in a canine pedigree constructed by mating dysplastic Labrador Retrievers with unaffected Greyhounds
Bibliographic record
Abstract
OBJECTIVE: To determine the genetic influence on expression of traits associated with canine hip dysplasia. ANIMALS: 193 dogs from an experimental canine pedigree. PROCEDURE: An experimental canine pedigree was developed for linkage analysis of hip dysplasia by mating dysplastic Labrador Retrievers with nondysplastic Greyhounds. A statistical model was designed to test the effects of Labrador Retriever and Greyhound alleles on age at detection of femoral capital epiphyseal ossification, 8-month distraction index, and 8-month dorsolateral subluxation score. RESULTS: The additive effect was significant for age at detection of femoral capital epiphyseal ossification. Restricted maximum likelihood estimates (+/-SD) for this trait were 6.4+/-1.95, 10.2+/-2.0, 10.8+/-3.1, 11.4+/-2.1, and 13.6+/-4.6 days of age for Greyhounds, Greyhound backcross dogs, F1 dogs, Labrador Retriever backcross dogs, and Labrador Retrievers, respectively. The additive effect was also significant for the distraction index. Estimates for this trait were 0.21+/-0.07, 0.29+/-0.15, 0.44+/-0.12, 0.52+/-0.18, and 0.6+/-0.17 for the same groups, respectively. For the dorsolateral subluxation score, additive and dominance effects were significant. Estimates for this trait were 73.5+/-4.1, 71.3+/-6.5, 69.1+/-6.0, 50.6+/-12.9, and 48.4+/-7.7%, respectively, for the same groups. CONCLUSIONS: In this canine pedigree, traits associated with canine hip dysplasia are heritable. Phenotypic differences exist among founder dogs of each breed and their crosses. This pedigree should be useful for identification of quantitative trait loci underlying the dysplastic phenotype.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".