A genome-wide case-control association study of dark cutting in beef cattle
Bibliographic record
Abstract
The genetic architecture of dark cutting was investigated with a case-control genome-wide association study on two groups of beef cattle analyzed separately and together (combined group). Groups I (n = 64) and II (n = 150) were genotyped using the 70K GeneSeek Genomic Profiler for Beef Cattle-HD and the 50K Illumina BovineSNP50v2 BeadChip, respectively. Dark cutting was analyzed as a binary trait (case versus control) using logistic regression in an additive model implemented in PLINK version 1.9. Significant loci were not identified when correcting for multiple testing (false discovery rate), suggesting that the trait is not controlled by genes with big effects, or the sample size was not large enough to detect these major genes. Regions harbouring single-nucleotide polymorphisms (SNPs) with a raw p < 0.01 using 1 MB window were analyzed for gene function using the ingenuity pathway analysis. For groups I, II, and the combined group, 449, 301, and 191 SNPs were identified, respectively. Genes identified were involved in pyruvic acid modification and release, 2-deoxyglucose clearance and disposal, sucrose recognition, energy production, and metabolism of carbohydrate. Although detected SNP associations require validation in a large population, results suggested the possibility for marker-assisted or genomic selection of beef cattle to reduce dark cutting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".