Genomic prediction and genetic parameter estimation for unsaturated and saturated fatty acids in Canadian dairy cattle
Bibliographic record
Abstract
The current study aimed to obtain the estimates of heritabilities and genetic correlations and the prediction abilities and accuracy of Bayesian GBLUP and Bayesian alphabet (BayesA, BayesB, BayesC) models for total and individual monounsaturated, polyunsaturated and saturated fatty acids from Canadian Holstein cows by using genome-wide SNP markers from genotyping-by-sequencing method. The heritability estimates were obtained from Bayesian GBLUP and Bayesian alphabet models. They ranged from 0.61 to 0.67 for total monounsaturated, from 0.35 to 0.45 for polyunsaturated and from 0.51 to 0.60 for saturated fatty acids, respectively. For thirty-three individual monounsaturated, polyunsaturated and saturated fatty acids, the heritability estimates ranged from 0.27 to 0.69 for individual monounsaturated, from 0.27 to 0.68 for individual polyunsaturated and from 0.35 to 0.69 for individual saturated fatty acids. These results indicated that total and individual monounsaturated, polyunsaturated and saturated fatty acids were under moderate genetic control and can be improved through genomic selection. The estimates of genetic correlations among total and individual monounsaturated, polyunsaturated and saturated fatty acids showed a moderate to high genetic relationships and pointed out the need for consideration of genetic relationships in successful genomic selection for fatty acids traits. The accuracies of BayesC and BayesA models were similar and better than that of GBLUP and BayesB models which indicated that fatty acids were determined by many genes having non-null effects, which are assumed to follow a univariate or multivariate Student's t distribution.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".