Developing a genome-wide selection model for genetic improvement of residual feed intake and carcass merit in a beef cattle breeding program
Bibliographic record
Abstract
Residual feed intake (RFI) and carcass merit (CM) are both complex traits emerging as critical targets for beef genetic improvement. RFI and CM traits are difficult and expensive to measure and genetic improvement for these traits through traditional selection methods is not very effective. Therefore, genome-wide selection using DNA markers may be a potential alternative for genetic improvement of these traits. In this study, the efficiency of a genome-wide selection model for genetic improvement of RFI and CM was assessed. The Illumina Bovine50K bead chip was used to genotype 922 beef cattle from the Kinsella Beef Research Ranch of the University of Alberta. A Bayes model and multiple marker regression using a stepwise method were used to conduct the association test. The number of significant SNP markers for carcass weight (CWT), carcass back fat (BF), carcass rib eye area (REA), carcass grade fat (GDF), lean meat yield (LMY), and residual feed intake (RFI) were 75, 54, 67, 57, 44 and 50, respectively. Bi-variate analysis of marker scores and phenotypes for all traits were made using DMU Software. The genetic parameter for each trait was estimated. The genetic correlations of marker score and phenotype for CWT, BF, REA, GDF, LMY and RFI were 0.75, 0.69, 0.87, 0.77, 0.78, and 0.85, respectively. The average prediction accuracies of phenotypic EBV for the six traits were increased by 0.05, 0.16, 0.24, 0.23, 0.17 and 0.19, respectively. The results of this study indicated that the two-trait marker-assisted evaluation model used was a suitable alternative of genetic evaluation for these traits in beef cattle.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".