Distribution of the Beta-Casein Gene Variants in Three Cattle Breeds Reared in Benin
Bibliographic record
Abstract
The beta-casein gene is one of the most functional genetic candidate that affect milk quality and composition traits. Among its variants, the A1/A2 are the most common. Therefore, the aim of this study was to identify the distribution of the Beta-casein gene variants (A1/A2) in three different cattle breeds in order to determine which of the breed produce a better milk for consumers’ health. 152 blood samples which comprises 72 (Muturu), 40 (Azawak) and 40 Girolando were used to carry out this study. Genomic DNA was extracted from the blood samples and each variant was subsequently amplified from the extracted DNA samples using an Allele-Specific PCR technique and then confirmed by running the PCR products on 1% agarose gel. The result showed that there were three genotypes (A1A1, A2A1 and A2A2) in the three breeds. The average percentage genotypic frequencies obtained from this study were 42.76%, 31.58% and 25.66% respectively for A1A1, A1A2 and A2A2 genotypes while the percentage allelic frequencies were 58% and 42% respectively for A1 and A2 allele. The genetic parameters of Azawak breed were higher than that of the other breeds, what implies that there was a higher polymorphism and genetic diversity in the Azawak breed in the beta-casein gene compare to the other breeds. The A2 beta-casein variant in milk has been found to be desirable for milk consumer’s health and nutrition. This study therefore showed that the Azawak breed provides a good potential for increasing this favorable allele through appropriate breeding techniques of cattle.
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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 | 0.000 |
| 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) | 0.001 | 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".