Genetic analysis for quality of frozen embryos produced by Holstein cattle donors in Canada
Bibliographic record
Abstract
The number of embryos produced by Holstein donors has been shown to be heritable, so it could be possible to genetically select for this trait to improve the efficiency of the assisted reproductive technology (ART) in dairy cattle. Another important parameter to consider for achieving good results from ART is embryo quality because embryos of good quality have more chance of producing live offspring. The possibility of using genetic selection for increasing the quality of embryo produced from ART has yet to be assessed. The objective of this study was, therefore, to perform a genetic analysis of embryo quality of Holstein donors in Canada using data recorded by Holstein Canada. The data set used was missing quality score data for embryos transferred fresh into a recipient, so the analyses were only performed for frozen embryos. With most traits in the Canadian dairy industry being evaluated with linear models, embryo quality was also evaluated with this class of models. However, considering the categorical nature of embryo quality, a threshold model was also evaluated. Embryo quality data were analyzed with either a univariate linear animal model or a univariate binomial threshold animal model. Genetic parameters estimated from the different models were comparable. A low heritability was found for the donor (0.04 ± <0.01) and the service sire (0.02 ± <0.01), but the repeatability estimate for the donor was higher (0.17), indicating that it was worthwhile to use a repeated records model. Overall, considering the low genetic parameters estimated, slow genetic progress is expected for the quality of frozen embryos produced by Canadian Holstein donors. Rank correlations were calculated between breeding values estimated from different models. High correlations were found between all models, indicating that no substantial re-ranking of the animals is expected from the different models. So, even though a threshold model is better suited for the analysis of categorical data, a linear model could be used for the analysis of embryo quality because it is less computationally demanding.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".