Accuracy of genomic predictions for feed efficiency traits of beef cattle using 50K and imputed HD genotypes1
Bibliographic record
Abstract
The accuracy of genomic predictions can be used to assess the utility of dense marker genotypes for genetic improvement of beef efficiency traits. This study was designed to test the impact of genomic distance between training and validation populations, training population size, statistical methods, and density of genetic markers on prediction accuracy for feed efficiency traits in multibreed and crossbred beef cattle. A total of 6,794 beef cattle data collated from various projects and research herds across Canada were used. Illumina BovineSNP50 (50K) and imputed Axiom Genome-Wide BOS 1 Array (HD) genotypes were available for all animals. The traits studied were DMI, ADG, and residual feed intake (RFI). Four validation groups of 150 animals each, including Angus (AN), Charolais (CH), Angus-Hereford crosses (ANHH), and a Charolais-based composite (TX) were created by considering the genomic distance between pairs of individuals in the validation groups. Each validation group had 7 corresponding training groups of increasing sizes ( = 1,000, 1,999, 2,999, 3,999, 4,999, 5,998, and 6,644), which also represent increasing average genomic distance between pairs of individuals in the training and validations groups. Prediction of genomic estimated breeding values (GEBV) was performed using genomic best linear unbiased prediction (GBLUP) and Bayesian method C (BayesC). The accuracy of genomic predictions was defined as the Pearson's correlation between adjusted phenotype and GEBV (), unless otherwise stated. Using 50K genotypes, the highest average achieved in purebreds (AN, CH) was 0.41 for DMI, 0.34 for ADG, and 0.35 for RFI, whereas in crossbreds (ANHH, TX) it was 0.38 for DMI, 0.21 for ADG, and 0.25 for RFI. Similarly, when imputed HD genotypes were applied in purebreds (AN, CH), the highest average was 0.14 for DMI, 0.15 for ADG, and 0.14 for RFI, whereas in crossbreds (ANHH, TX) it was 0.38 for DMI, 0.22 for ADG, and 0.24 for RFI. The of GBLUP predictions were greatly reduced with increasing genomic average distance compared to those from BayesC predictions. The results indicate that 50K genotypes, used with BayesC, are more effective for predicting GEBV in purebred cattle. Imputed HD genotypes found utility when dealing with composites and crossbreds. Formulation of a fairly large training set for genomic predictions in beef cattle should consider the genomic distance between the training and target populations.
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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 | 0.000 | 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".