Whole‐genome QTL scan for ultrasound and carcass merit traits in beef cattle using Bayesian shrinkage method
Bibliographic record
Abstract
Fine mapping of quantitative trait loci (QTL) for 16 ultrasound measurements and carcass merit traits that were collected from 418 hybrid steers was conducted using 1207 SNP markers covering the entire genome. These SNP markers were evaluated using a Bayesian shrinkage estimation method and the empirical critical significant thresholds (α = 0.05 and α = 0.01) were determined by permutation based on 3500 permuted datasets for each trait to control the genome-wide type I error rates. The analyses identified a total of 105 QTLs (p < 0.05) for seven ultrasound measure traits including ultrasound backfat, ultrasound marbling and ultrasound ribeye area and 113 QTLs for seven carcass merit traits of carcass weight, grade fat, average backfat, ribeye area, lean meat yield, marbling and yield grade. Proportion of phenotypic variance accounted for by a single QTL ranged from 0.06% for mean ultrasound backfat to 4.83% for carcass marbling (CMAR) score, while proportion of the phenotypic variance accounted for by all significant (p < 0.05) QTL identified for a single trait ranged from 4.54% for carcass weight to 23.87% for CMAR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".