Modeling heterotic effects in beef cattle using genome-wide SNP-marker genotypes1
Bibliographic record
Abstract
An objective of commercial beef cattle crossbreeding programs is to simultaneously optimize use of additive (breed differences) and non-additive (heterosis) effects. A total of 6,794 multibreed and crossbred beef cattle with phenotype and Illumina BovineSNP50 genotype data were used to predict genomic heterosis for growth and carcass traits by applying two methods assumed to be linearly proportional to heterosis. The methods were as follows: 1) retained heterozygosity predicted from genomic breed fractions (HET1) and 2) deviation of adjusted crossbred phenotype from midparent value (HET2). Comparison of methods was based on prediction accuracy from cross-validation. Here, a mutually exclusive random sampling of all crossbred animals (n = 5,327) was performed to form five groups replicated five times with approximately 1,065 animals per group. In each run within a replicate, one group was assigned as a validation set, while the remaining four groups were combined to form the reference set. The phenotype of the animals in the validation set was assumed to be unknown; thus, it resulted in every animal having heterosis values that were predicted without using its own phenotype, allowing their adjusted phenotype to be used for validation. The same approach was used to test the impact of predicted heterosis on accuracy of genomic breeding values (GBV). The results showed positive heterotic effects for growth traits but not for carcass traits that reflect the importance of heterosis for growth traits in beef cattle. Heterosis predicted by HET1 method resulted in less variable estimates that were mostly within the range of estimates generated by HET2. Prediction accuracy was greater for HET2 (0.37-0.98) than HET1 (0.34-0.43). Proper consideration of heterosis in genomic evaluation models has debatable effects on accuracy of EBV predictions. However, opportunity exists for predicting heterosis, improving accuracy of genomic selection, and consequently optimizing crossbreeding programs in beef cattle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".