<i>FTO</i> and <i>IRX3</i> Genes are Not Promising Markers for Obesity in Labrador Retriever Dogs
Bibliographic record
Abstract
Abstract Obesity is a serious problem in numerous dog breeds, but knowledge of its hereditary background is scarce. On the contrary, numerous DNA polymorphisms associated with human obesity have been identified, with the strongest effect being demonstrated for FTO gene. We used targeted next-generation sequencing (tNGS) to search for polymorphisms in the region harboring FTO and IRX3 in 32 Labrador dogs. Moreover, we investigated the selected regions of FTO and IRX3 , orthologous to the human regions associated with obesity, in 165 Labradors. For all dogs, the following information was available: age, sex, gonadal status, body weight, and body conformation score (BCS). The use of tNGS revealed 12,217 polymorphisms, but none of these obtained significance when lean and obese dogs were compared. Study of two SNPs in the 5’-flanking region of FTO in 165 dogs – creating two upstream reading frames (uORFs) – also showed no association with body weight and BCS but suggested the need for improvement in FTO annotation. No polymorphism was found in the 5’UTR of IRX3. Additionally, no differences of CpG islands methylation status between lean and obese dogs were found. Our study suggests that FTO and IRX3 are not useful markers of obesity in Labrador dogs.
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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 | 0.000 |
| 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) | 0.002 | 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".