43 Single and Multiple-breed Genomic Predictions for Conformation Traits of Canadian Dairy Goats
Bibliographic record
Abstract
Abstract The use of multiple-breed models can increase the accuracy of estimated breeding values (EBV) when few phenotypes are available for a trait. However, pooling breeds is not always beneficial for genomic evaluations due to the low consistency of gametic phase between individual breeds. The objective of this study was to compare the expected gain in accuracy of single-step genomic breeding values (GEBV) for conformation traits of Canadian Alpine and Saanen goats predicted using single and multiple-breed models. The traits considered were body capacity, dairy character, feet and legs, fore udder, general appearance, rear udder, suspensory ligament, and teats, all recorded by trained classifiers, using a 1 to 9 scale. The full datasets included a total of 7,500 phenotypes for each trait (5,158 Alpine and 2,342 Saanen) and 1,707 50K genotypes (833 Alpine, 874 Saanen). Standard errors of prediction (SEP) were obtained for EBV and GEBV predicted using single-trait animal models on full or validation datasets. Breed difference was accounted for as a fixed effect in the multiple-breed models. Average theoretical accuracies were calculated from the SEP. For Saanen, with fewer records, expected accuracies of EBV and GEBV for the validation animals (selection candidates) were consistently higher for the multiple-breed models. Trait specific gains in theoretical accuracy of GEBV relative to EBV for the selection candidates ranged from 30 to 48% for Alpine and 41 to 61% for Saanen. Averaged across all traits, GEBV predicted from the full dataset were 32 to 38% more accurate than EBV for genotyped animals and the largest gains were found for does without conformation records (49 to 55%) and bucks without daughter records (56 to 82%). Overall, the implementation of genomic selection would substantially increase selection accuracy for young breeding candidates and, consequently, the rate of genetic improvement for conformation traits in Canadian dairy goats.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".