Identification and fine mapping of quantitative trait loci for backfat on bovine chromosomes 2, 5, 6, 19, 21, and 23 in a commercial line of Bos taurus1
Bibliographic record
Abstract
Backfat thickness is one of the major quantitative traits that affects carcass quality in beef cattle. In this study, we identified and fine-mapped QTL for backfat EBV on bovine chromosomes 2, 5, 6, 19, 21, and 23 using an identical-by-descent haplotype-sharing analysis in a commercial line of Bos taurus. Eleven haplotypes were found to have significant associations with backfat EBV at the comparison-wise P-value threshold, and one at the chromosome-wise P-value threshold on bovine chromosomes 5, 6, 19, 21, and 23. On average, the 12 significant haplotypes had an effect of 0.62 SD on backfat EBV, ranging from 0.38 SD to 1.33 SD. The 12 significant haplotypes spanned nine chromosomal regions, one on chromosome 5 (65.4 to 70.0 cM), three on 6 (8.2 to 11.8 cM, 63.6 to 68.1 cM, and 81.5 to 83.0 cM), three on 19 (4.8 to 15.9 cM, 39.4 to 46.5 cM, and 65.7 to 99.5 cM), one on 21 (46.1 to 53.1 cM), and one on 23 (45.1 to 50.9 cM). Among the nine chromosomal regions, six were new QTL regions and three showed remarkable agreement with QTL regions that were previously reported. Eight of the nine QTL regions were localized to less than or close to 10 cM in genetic distance. The results provide a useful reference for further positional candidate gene research and marker-assisted selection for backfat.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".