Sequence analysis of three canine adipokine genes revealed an association between <i><scp>TNF</scp></i> polymorphisms and obesity in Labrador dogs
Bibliographic record
Abstract
Obesity is an emerging health problem in purebred dogs. Due to their crucial role in energy homeostasis control, genes encoding adipokines are considered candidate genes, and their variants may be associated with predisposition to obesity. Searching for polymorphism was carried out in three adipokine genes (TNF, RETN and IL6). The study was performed on 260 dogs, including lean (n = 109), overweight (n = 88) and obese (n = 63) dogs. The largest cohort was represented by Labrador Retrievers (n = 136). Altogether, 24 novel polymorphisms were identified: 12 in TNF (including one missense SNP), eight in RETN (including one missense SNP) and four in IL6. Distributions of five common SNPs (two in TNF, two in RETN and one in IL6) were further analyzed with regard to body condition score. Two SNPs in the non-coding parts of TNF (c.-40A>C and c.233+14G>A) were associated with obesity in Labrador dogs. The obtained results showed that the studied adipokine genes are highly polymorphic and two polymorphisms in the TNF gene may be considered as markers predisposing Labrador dogs to obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".