Evaluation of a fibrillin 2 gene haplotype associated with hip dysplasia and incipient osteoarthritis in dogs
Bibliographic record
Abstract
OBJECTIVE: To determine whether a mutation in the fibrillin 2 gene (FBN2) is associated with canine hip dysplasia (CHD) and osteoarthritis in dogs. ANIMALS: 1,551 dogs. Procedures-Hip conformation was measured radiographically. The FBN2 was sequenced from genomic DNA of 21 Labrador Retrievers and 2 Greyhounds, and a haplotype in intron 30 of FBN2 was sequenced in 90 additional Labrador Retrievers and 143 dogs of 6 other breeds. Steady-state values of FBN2 mRNA and control genes were measured in hip joint tissues of fourteen 8-month-old Labrador Retriever-Greyhound crossbreeds. RESULTS: The Labrador Retrievers homozygous for a 10-bp deletion haplotype in intron 30 of FBN2 had significantly worse CHD as measured via higher distraction index and extended-hip joint radiograph score and a lower Norberg angle and dorsolateral subluxation score. Among 143 dogs of 6 other breeds, those homozygous for the same deletion haplotype also had significantly worse radiographic CHD. Among the 14 crossbred dogs, as the dorsolateral subluxation score decreased, the capsular FBN2 mRNA increased significantly. Those dogs with incipient hip joint osteoarthritis had significantly increased capsular FBN2 mRNA, compared with those dogs without osteoarthritis. Dogs homozygous for the FBN2 deletion haplotype had significantly less FBN2 mRNA in their femoral head articular cartilage. CONCLUSIONS AND CLINICAL RELEVANCE: The FBN2 deletion haplotype was associated with CHD. Capsular gene expression of FBN2 was confounded by incipient secondary osteoarthritis in dysplastic hip joints. Genes influencing complex traits in dogs can be identified by genome-wide screening, fine mapping, and candidate gene screening.
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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".