Association of <i>MSTN</i> gene polymorphism (C354T) with performance traits in raccoon dogs
Bibliographic record
Abstract
The aim of the study was to identify polymorphisms within nuclear DNA genes and determine their association with the performance traits of farmed raccoon dogs. The study involved 354 animals and their breeding documentation data. Myostatin (MSTN), insulin-like growth factor 1 (IGF1), and growth hormone (GHR) genes were amplified, sequenced, and subjected to bioinformatics and statistical analysis. Estimation of variance components was performed with the residual maximum likelihood (REML) method, and best linear unbiased prediction (BLUP) of the breeding values was carried out. The predictors of the traits of direct additive, maternal additive, and random-specific maternal environmental effects were analysed. Within the nucleotide sequences of the analysed genes, one silent single nucleotide polymorphism (SNP) was identified in exon 1 of the MSTN gene. The analysis of the variance of the fixed-specific maternal environmental effect revealed statistically significant differences in the body weight among raccoon dogs with alternative polymorphisms. The mean effects of the estimators of polymorphism addition and dominance measured by MSTN gene regression had mostly a significant impact on the level of the traits estimated in animals. There was statistically significant association of the polymorphism in the MSTN gene with the body weight in raccoon dogs, which validates selection thereof as a candidate gene for this economically most important performance trait of raccoon 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 | 0.001 |
| 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) | 0.001 | 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".