Association Analysis Between Variants in Bovine Progesterone Receptor Gene and Superovulation Traits in Chinese Holstein Cows
Bibliographic record
Abstract
The objective of this study was to identify a predictor to forecast superovulation response on the basis of associations between superovulation performance and gene polymorphism. The PCR-RFLP method was applied to detect two reported single nucleotide polymorphisms (SNPs) of G59752C and T81637C (rs41614030) located in introns 3 and 4 of the bovine progesterone receptor (PGR) gene in 171 Chinese Holstein cows treated for superovulation and evaluate its associations with superovulation traits. In polymorphic locus 81637, all cows without superovulation response were g.81637TC and g.81637TT genotypes. Association analysis showed that these two SNPs had significant effects on the total number of ova (TNO) (p<0.05), and the T81637C polymorphism was significantly associated with the number of transferable embryos (p<0.05). In addition, significant additive effects (p<0.05) on TNO were detected in the polymorphisms of G59752C and T81637C. These results showed for the first time that the G59752C and T81637C polymorphisms in PGR gene were associated with superovulation traits and indicated that PGR gene can be used as a predictor for superovulation in Chinese Holstein cows.
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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 | 0.000 | 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".