Association analysis for feed efficiency traits in beef cattle using preserved haplotypes
Bibliographic record
Abstract
This study reports a genome wide scan for chromosome regions and their haplotypes that significantly associated with average daily gain (ADG), dry matter intake (DMI), and residual feed intake (RFI) in beef cattle. The study used data from 597 Angus, 450 Charolais, and 616 crossbred beef cattle, and the Illumina Bovine SNP50 beadchip. Extended haplotype homozygosity was used to identify chromosome regions that had been recently selected for in the three groups of animals. Such regions in the crossbreds were tested for association with ADG, DMI, and RFI. At false discovery rates of 5% and 10%, there were six and eight chromosome regions showing significant associations with the traits, respectively. At nominal significance levels (at least P < 0.05), 23 regions with a total number of 31 haplotypes were found significantly associated with at least one of the three traits. The proportion of phenotypic variance explained by these 23 regions varied depending on the trait; the highest proportion for ADG, DMI, and RFI was 13.50%, 9.92%, and 2.64%, respectively. Most of the haplotypes affected single traits, except for GAA (BTA4), GCG (BTA7), and TAGT (BTA12) that affected multiple traits. Thirty-six quantitative trait loci for 16 production traits, from the current literature, covered fully or in part the 23 chromosome regions. The findings from this study might be an important contribution to the current knowledge of the beef cattle genome and to the effective identification of causative genes associated with important traits in cattle.
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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 | 0.001 |
| 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".