Evaluation of quantitative trait loci for hip dysplasia in Labrador Retrievers
Bibliographic record
Abstract
OBJECTIVE: To identify the quantitative trait loci (QTL) that contribute to hip dysplasia in dogs. ANIMALS: 192 Labrador Retrievers. PROCEDURES: Hip dysplasia was measured by use of the Norberg angle (NA), dorsolateral subluxation (DLS) score, and distraction index (DI). Genome-wide screening was conducted by use of 276 unique microsatellites. Linkage analysis was performed with a variance-based linear model. Logarithm of the odds (LOD) scores were reported when values were > 2.0. RESULTS: Canis familiaris autosomes (CFAs) 01, 02, 10, 20, 22, and 32 harbored significant QTL at LOD scores > 2.0. Among the 6 QTL, the QTL on CFA02 had not been reported to harbor QTL for hip dysplasia. The highest LOD score of 3.32 on CFA20 contributed to the second principal component of the DLS score and NA of the right hip joint. The QTL that was mapped on CFA01 (LOD score of 3.13 at 55 centimorgans) was located on the same chromosome reported to harbor a QTL for hip dysplasia in Portuguese Water Dogs and German Shepherd Dogs. In this study, CFAs 10, 20, 22, and 32 harbored QTL for hip dysplasia that have been identified in a Labrador Retriever-Greyhound pedigree and in German Shepherd Dogs. CONCLUSIONS AND CLINICAL RELEVANCE: Multiple QTL were clearly involved with hip dysplasia. Identification of these QTL will enable fine-resolution mapping and subsequent assessment of candidate genes within the refined intervals to enable researchers to develop genetic screening tests and preventative and novel therapeutic regimens.
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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 | 0.001 | 0.000 |
| 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".