Mate selection strategies to exploit across‐ and within‐breed dominance variation
Bibliographic record
Abstract
Summary In multi‐breed livestock populations, dominance variance is found both between and within breeds for many economically important traits. Mate selection strategies were developed to exploit both types of dominance variation simultaneously, with the aim of maximizing genetic merit of progeny. The extended super‐breed model, in which breeds are viewed as groups of related and inbred animals within a ‘super‐breed’, was used to predict individual additive and dominance effects for use in mate selection. Performance of mate selection was assessed under a range of relative values of additive and dominance variances for one generation of breeding. Mate selection on total progeny merit, including additive effects, individual dominance effects, and value of heterosis, was the optimal breeding strategy at all values of (co)variance components, with improvements in total progeny performance of up to 12.5 % over truncation selection followed by random mating when dominance variance was large relative to total genetic variance. Improvement in progeny merit from mate selection, relative to truncation selection, followed by random mating or truncation selection, followed by mate allocation, was particularly great (up to 53 %) when there was considerable heterosis. Improvements were small if dominance variance was small relative to total genetic variance, and heterosis was low. If the target population is large, full mate selection on total progeny merit is computationally demanding, and unlikely to be practical. Alternative, less computationally demanding strategies made nearly optimal selection and mating decisions at some parameter estimates. Integrating multi‐breed genetic evaluation, using a superbreed model, with mate selection provides a powerful framework for the design of breeding programmes which exploit available sources of genetic merit.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".