Polymorphism and methylation of the MC4R gene in obese and non-obese dogs
Bibliographic record
Abstract
The dog is considered to be a useful biomedical model for human diseases and disorders, including obesity. One of the numerous genes associated with human polygenic obesity is MC4R, encoding the melanocortin 4 receptor. The aim of our study was to analyze polymorphisms and methylation of the canine MC4R in relation to adiposity. Altogether 270 dogs representing four breeds predisposed to obesity: Labrador Retriever (n = 187), Golden Retriever (n = 38), Beagle (n = 28) and Cocker Spaniel (n = 17), were studied. The dogs were classified into three groups: lean, overweight and obese, according to the 5-point Body Condition Score (BCS) scale. In the cohort of Labradors a complete phenotypic data (age, sex, neutering status, body weight and BCS) were collected for 127 dogs. The entire coding sequence as well as 5' and 3'-flanking regions of the studied gene were sequenced and six polymorphic sites were reported. Genotype frequencies differed considerably between breeds and Labrador Retrievers appeared to be the less polymorphic. Moreover, distribution of some polymorphic variants differed significantly (P < 0.05) between small cohorts with diverse BCS in Golden Retrievers (c.777T>C, c.868C>T and c.*33C>G) and Beagles (c.-435T>C and c.637G>T). On the contrary, in Labradors no association between the studied polymorphisms and BCS or body weight was observed. Methylation analysis, using bisulfite DNA conversion followed by Sanger sequencing, was carried out for 12 dogs with BCS = 3 and 12 dogs with BCS = 5. Two intragenic CpG islands, containing 19 cytosines, were analyzed and the methylation profile did not differ significantly between lean and obese animals. We conclude that an association of the MC4R gene polymorphism with dog obesity or body weight is unlikely, in spite of the fact that some associations were found in small cohorts of Beagles and Golden Retrievers. Also methylation level of this gene is not related with dog adiposity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".