Estimation of variance components for carcass traits in <scp>J</scp>apanese <scp>B</scp>lack cattle using 50<scp>K SNP</scp> genotype data
Bibliographic record
Abstract
Genomic selection using high-density single nucleotide polymorphism (SNP) genotype data may accelerate genetic improvements in livestock animals. In this study, we attempted to estimate the variance components of six carcass traits in fattened Japanese Black steers using SNP genotype data. Six hundred and seventy-three steers were genotyped using an Illumina Bovine SNP50 BeadChip and phenotyped for cold carcass weight, ribeye area, rib thickness, subcutaneous fat thickness, estimated yield percent and marbling score. Additive polygenic variance and the variance attributable to a set of SNPs that had statistically significant effects on the trait were estimated via Gibbs sampling with two models: (i) a model with the chosen SNPs and the additive polygenic effects; and (ii) a model with the polygenic effects alone. The proportion of the estimated variance attributable to the SNPs became higher as the number of SNP effects that fit increased. High correlations between breeding values estimated with the model containing the polygenic effect alone and those estimated by chosen SNPs were obtained. No fraction of the total genetic variance was explained by SNPs associated with the trait at P ≥ 0.1. Our results suggest that for the carcass traits of Japanese Black cattle, a maximum of half of the total additive genetic variance may be explained by SNPs between 100 several tens to several 100s.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".