Polymorphisms of Candidate Genes Associated with Growth and Carcass Traits in Canadian Duroc Pigs
Bibliographic record
Abstract
Background: Selection based on traits such as average daily gain (ADG) and carcass quality in commercial pig populations have drawn attention of swine breeders due to the correlation with growth and consumer preference for carcass composition, respectively. The association between melanocortin 4 receptor (MC4R) gene, which encodes the G-protein-coupled receptor and ADG and carcass quality, have been well documented. The study was conducted to determine the effects of MC4R/TaqI and PIT-1/RsaI polymorphisms on growth and carcass quality traits in total of 576 Duroc pigs at national breeding farms of Vietnam. Methods: After the performance test, the desired traits including ADG, IF and LD were measured. ADG (g/day) was calculated as the live weight divided by the number of days from birth to 100 kg, BF and LD can be captured at position P2 (6-8 cm away from body midline at the last rib level) with Ultrasound machine Aloska SSD 500V. Genomic DNA were collected and genotyped to observe the polymorphism of MC4R and PIT-1. Result: Three genotypes AA, AG andGG of MC4R gene and AA, AB and BB of PIT-1 gene were found in the studied pig population. The observed frequencies of AA, AG and GG were 0.09, 0.41 and 0.50 (MC4R gene) and for AA, AB and BB were 0.37, 0.47 and 0.16, respectively. The G allele (MC4R) and B allele (PIT-1) have more positive effects on traits of ADG, BF and LD. Specifically, in MC4R gene, the individuals carrying GG genotype had higher ADG by 50 gram and lower BF by 1.4 mm than AA genotype. In PIT-1 gene, pigs carrying BB genotype had higher ADG and LD than AA genotype by 37 gram and by 1.9 mm, respectively. Therefore, the increased selection of G allele, GG genotype (MC4R) and of B allele, BB genotype (PIT-1) should be considered to contribute to the improvement of ADG, BF and LD traits in Duroc population in current study.
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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 | 0.001 | 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".