Genetic variation of <i>PRLR</i> gene and association with milk performance traits in dairy cattle
Bibliographic record
Abstract
Prolactin plays an important regulatory function in mammary gland development, milk secretion, and expression of milk protein genes. Prolactin exerts diverse functions in its several target tissues through specific membrane receptors. Hence, prolactin receptor (PRLR) gene is a potential quantitative trait locus and genetic marker of milk performance traits in dairy cattle. In this study, the genetic polymorphisms of part of the PRLR gene were detected in a population of 385 cows of Chinese Holstein dairy cattle to determine the association between PRLR gene and milk performance traits. Using polymerase chain reaction-single-strand conformation polymorphism (PCR-SSCP) and sequencing, two alleles in exon 3 and three alleles in exon 7 of PRLR gene were identified. Four of the detected SNPs lead to the amino acid substitution. Statistical results indicated that PRLRE3 and PRLRE7 loci were significantly associated with milk yield (P < 0.01), and fat percentage (P < 0.05). The cows with genotypes AB had significantly higher milk yield (P < 0.05), and cows with genotypes AA had significantly higher fat percentage (P < 0.05) in locus PRLRE3. The cows with genotypes BB had significantly higher milk yield (P < 0.05), and fat percentage (P < 0.05) in locus PRLRE7. In addition, based on the nine genotypes constructed from 13 combined genotypes, the association analysis between combined genotypes and milk performance traits was carried out. Results show that the cows with combined genotypes ABBB had significantly higher milk yield and fat percentage. Our finding implies that PRLRE3 and PRLRE7 loci of the PRLR gene would be useful genetic markers in selection program on milk performance traits in Holsteindairy cattle. Key words: Holstein dairy cattle, prolactin receptor, milk performance traits
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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 | 0.001 | 0.001 |
| 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".