Genome wide association study in Swedish Labrador retrievers identifies genetic loci associated with hip dysplasia and body weight
Bibliographic record
Abstract
Abstract Genome wide association studies (GWAS) have been utilized to identify genetic risk loci associated with both simple and complex inherited disorders. Here, we performed a GWAS in Labrador retrievers to identify genetic loci associated with hip dysplasia and body weight. Hip dysplasia scores were available for 209 genotyped dogs. We identified a significantly associated locus for hip dysplasia on chromosome 24, with three equally associated SNPs (p = 4.3 × 10–7) in complete linkage disequilibrium located within NDRG3, a gene which in humans has been shown to be differentially expressed in osteoarthritic joint cartilage. Body weight, available for 85 female dogs, was used as phenotype for a second analysis. We identified two significantly associated loci on chromosome 10 (p = 4.5 × 10–7) and chromosome 31 (p = 2.5 × 10–6). The most associated SNPs within these loci were located within the introns of the PRKCE and CADM2 genes, respectively. PRKCE has been shown to play a role in regulation of adipogenesis whilst CADM2 has been associated with body weight in multiple human GWAS. In summary, we identified credible candidate loci explaining part of the genetic inheritance for hip dysplasia and body weight in Labrador retrievers with strong candidate genes in each locus previously implicated in the phenotypes investigated.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".