P5064 Accuracy of genome-wide predictions of heterosis in beef cattle using 50K genotypes
Bibliographic record
Abstract
Commercial beef cattle production makes use of crossbreeding to exploit heterosis and breed complementarity. Developing a reliable method of heterosis prediction would greatly improve the efficiency of crossbreeding schemes. Also, there is potential for improving accuracy of crossbred breeding values by accounting for heterosis in the genetic evaluation model. The objective of this study was to evaluate the accuracy of genomic prediction of heterosis using a cross-validation approach and to test the impact on genomic estimated breeding value (GEBV) accuracies in beef cattle. A total of 6794 multi-breed and crossbred beef cattle with phenotype and Illumina BovineSNP50 (50K) genotype data were used. Details of breed description, population structure and data editing were reported by [1]. The studied traits included growth and carcass traits as defined in Table 1. Three methods that utilized genome-wide SNP data were applied to predict heterosis: 1) average heterozygosity across loci (H), 2) dominance deviations from dominance relationship matrix (D) and 3) deviation of crossbred phenotype from mid-parent value using information from genomic breed proportions (HV) obtained from the admixture software [2]. A mutually exclusive random sampling of all animals was performed to form 5-groups replicated 5 times with an average of 1359 animals per group. In each analysis within a replicate, one group was dedicated as the 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 predicted without using its own phenotype, allowing their phenotype to be used for validation. The same approach was applied for testing the accuracy of GEBV when accounting for heterosis predicted from the three methods. Our results showed that the best predictor of genomic heterosis for beef carcass and growth traits was HV (Table 1) with accuracy ranging from 0.46 to 0.99. Inclusion of heterosis from the HV method in genomic evaluation improved accuracy of GBV up to 20%. Thus, the opportunity exists for predicting heterosis, improving accuracy of genomic selection and subsequently, optimizing crossbreeding program in beef cattle. [1.] Lu, D., Akanno, E. C. Crowley, J. J., Plastow, G., et al. 2016. J. Anim. Sci. doi:10.2527/jas.2015-0126. [2.] Alexander, D. H., Novembre, J., and Lange, K. 2009. Genome Research, 19:1655–1664.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".