Genetic association of variations in the kappa-casein and β-lactoglobulin genes with milk traits in girolando cattle
Bibliographic record
Abstract
SUMMARY In dairy farm animals, one the most important goal of the selection is the improvement of milk yield and composition. Several studies have demonstrated that the candidate genes of the kappa-casein (CSN3) and β - lactoglobulin (β-LG) are associated with milk yield, milk quality and health traits in dairy animals. Therefore the aim of this study was to detect polymorphisms in CSN3 and β-LG genes and its association with milk yield in up to 305 days (305MY) and predicted transmission capacity (PTA) for 305MY in Girolando cattle. Totally, 138 bulls and 729 cows (n=867) were sampled. The genotypes of both genes were obtained by the PCR-RFLP method using HinfI and HaeIII enzymes for CSN3 and β-LG genes, respectively. Statistical results revealed two alleles A and B for both genes. The genotypes and alleles more frequents for CSN3 and β-LG genes were respectively: AA (0.7324) and A (0.8558), and AB (0.4827) and A (0.5017). The x2 test revealed that the two loci were at Hardy-Weinberg equilibrium (p<0.001). The allele substitution effects for the variants were not significant on 305MY and PTA for 305MY (p>0.05). The allele variants of β-LG and CSN3 might be more investigated before include them into future breeding schemes designed for Girolando dairy cattle with objective of improving milk traits as milk yield in up to 305 days (305MY) and predicted transmission capacity (PTA) for 305MY..
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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 | 0.000 | 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) | 0.001 | 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".