Genetic parameters and genomic insights for meat colour traits of Canadian crossbred beef cattle
Bibliographic record
Abstract
Utilizing genomic tools in breeding programs accelerates genetic progress, particularly for difficult/expensive to measure traits such as meat colour. This study aimed to estimate genetic parameters and identify genomic regions and candidate genes associated with meat colour traits in Canadian crossbred beef cattle. Heritability estimates for lightness ( L* ), chroma ( c* ), and hue angle ( Hu) were 0.44, 0.20, and 0.57, respectively. L* and Hu were highly phenotypically (0.86) and genetically (0.98) correlated. The genome-wide association revealed a total of 30, 24, and 25 SNP windows explaining >0.5% additive genetic variance for L*, c*, and Hu, respectively. These SNP windows collectively explained >20% of the genetic variance for the three meat colour traits. The genes GNPDA2, CALCRL, CALM1, KIT, PRKN, and PRKAA2 are the most promising candidates associated with L*, TEX37, ADAMTS9, PRKACB, TTLL7, TFAM, AGT, ACAT2, and PPP1R3C with c*, and MMP2, AKR1B1, AKR1B10, and CYP7A1 with Hu. These candidate genes are mainly involved in energy metabolism pathways, among which the most important are glucose, fatty, and lipid metabolism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".