Uncovering structural variants associated with body weight and obesity risk in labrador retrievers: a genome-wide study
Bibliographic record
Abstract
Although obesity in the domestic dog (Canis lupus familiaris) is known to decrease well-being and shorten lifespan, the genetic risk variants associated with canine obesity remain largely unknown. In our study, which focused on the obesity-prone Labrador Retriever breed, we conducted a genome-wide analysis to identify structural variants linked to body weight and obesity. Obesity status was based on a 5-point body condition score (BCS) and the obese dog group included all dogs with a BCS of 5, along with dogs with the highest body weight within the BCS 4 group. Data from whole-gene sequencing of fifty dogs, including 28 obese dogs, were bioinformatically analyzed to identify potential structural variants that varied in frequency between obese and healthy dogs. The seven most promising variants were further analyzed by droplet digital PCR in a group of 110 dogs, including 63 obese. Our statistical evidence suggests that common structural mutations in or near six genes, specifically ALPL, KCTD8, SGSM1, SLC12A6, RYR3, and VPS26C, may contribute to the variability observed in body weight and body condition scores among Labrador Retriever dogs. These findings emphasize the need for additional research to validate the associations and explore the specific functions of these genes in relation to canine obesity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".