Genome-wide association analyses for growth and feed efficiency traits in beef cattle1
Bibliographic record
Abstract
A genome-wide association study using the Illumina 50K BeadChip included 38,745 SNP on 29 BTA analyzed on 751 animals, including 33 purebreds and 718 crossbred cattle. Genotypes and 6 production traits: birth weight (BWT), weaning weight (WWT), ADG, DMI, midtest metabolic BW (MMWT), and residual feed intake (RFI), were used to estimate effects of individual SNP on the traits. At the genome-wide level false discovery rate (FDR < 10%), 41 and 5 SNP were found significantly associated with BWT and WWT, respectively. Thirty-three of them were located on BTA6. At a less stringent significance level (P < 0.001), 277 and 27 SNP were in association with single traits and multiple traits, respectively. Seventy-three SNP on BTA6 and were mostly associated with BW-related traits, and heavily located around 30 to 50Mb. Markers that significantly affected multiple traits appeared to impact them in same direction. In terms of the size of SNP effect, the significant SNP (P < 0.001) explained between 0.26 and 8.06% of the phenotypic variation in the traits. Pairs of traits with low genetic correlation, such as ADG vs. RFI or DMI vs. BWT, appeared to be controlled by 2 groups of SNP; 1 of them affected the traits in same direction, the other worked in opposite direction. This study provides useful information to further assist the identification of chromosome regions and subsequently genes affecting growth and feed efficiency traits in beef 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".