Molecular variants of bovine GH and GHR and their association with milk production traits in Canadian Holstein bulls
Bibliographic record
Abstract
In dairy cattle, treatment with exogenous growth hormone (GH) affects growth and function of mammary gland. The actions of GH are mediated via interaction with GH receptors (GHR). The first step in signal transduction is homodimerization of two GHR molecules by GH. This step is critical since mutation in either GH or GHR can block dimerization and thus target cell activation. However, association between milk related traits and combination of GH and GHR variations are not known. Accordingly, DNA genotypes in the GH and GHR genes were investigated for association with milk, fat and protein lactation yields in Holsteins. The marker data were obtained on 873 progeny tested bulls by using PCR-RFLP and PCR-SSCP analysis. There were five markers in GH and three in GHR. Estimated breeding values (EBVs) were obtained from Canadian Dairy Network for milk, fat, and protein lactation yields for the 873 genotyped bulls. There was significant difference among GH6.1 alleles (C-to-G transversion at position 2141) for the milk yield (P < 0.05) and protein yield (p < 0.05). There were significant differences in GHR AluI (A-to-T transversion at -1182) for milk (p < 0.05) and fat (p < 0.05), and GHR StuI (C-to-T transversion at -232) for fat (p < 0.0001) and protein (p < 0.05). Allele frequencies for GH6.1 (C), GHR AluI (A) and GHR StuI (C) alleles in bulls genotyped were 0.95, 0.63 and 0.95, respectively. Bulls with GH6.1 (C/G) genotype had higher milk EBV (p < 0.05) compared to C/C bulls. Bulls with GHR AluI (A/A) genotype had higher milk EBV (p < 0.01) and fat EBVs (p < 0.05). Bulls with StuI (C/C) genotype had higher fat EBV (p < 0.0001) and protein EBV (p < 0.05) compared to StuI (C/T). This study indicates that the combination of GH and GHR markers could serve as a tool to aid in selection for improving milk, fat, and protein production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".