283 Validation of Quantitative Trait Loci Associated with Grazing Distribution Traits in Beef Cattle Using Bayes C.
Bibliographic record
Abstract
Concentrated grazing may destroy wildlife habitat, reduce forage harvest, and degrade riparian areas; therefore, it is necessary to develop tools to improve grazing distribution for extensive pastures in beef production systems. A previous genome-wide association study identified 5 QTL on BTA 4, 12, 17, and 29 that were associated with grazing distribution indices (rolling and rough). The objective of this study was to validate these QTL using additional association analyses with a Bayesian approach. Global positioning system (GPS) technology was used to collect grazing distribution phenotypes from 80 beef cows on 5 ranches in New Mexico, Montana, and Arizona. Percent slope, elevation, and distance from water were calculated using GPS coordinates and incorporated into two terrain-use indices (rough and rolling). Cows were genotyped with 777,962 SNP and after applying standard SNP quality filters, 733,713 SNP from 75 cows were available for analysis. A single chromosome (BTA 4, 12, 17, 29) association analysis was performed for both the rough and rolling indices using Bayes C (within the BOLT software package. The posterior inclusion probability (PIP) for each SNP was estimated. The association between rs109619368 on BTA 17 and the rough index was confirmed (PIP = 2.1%); however, the QTL on BTA 4, 12, and 29 were not verified. To further investigate this, 50 SNP spanning 0.2 MB on BTA 29 were analyzed with the rolling index. Two SNP were confirmed in this analysis: rs42161939 (PIP = 54.9%) and rs43744222 (PIP = 21.6%). The Bayesian approach applied in this study, in which markers are simultaneously fit in the model, revealed 3 QTL concordant with previous analyses which used a single-marker regression approach. These results suggest that grazing distribution traits are under genetic control; however, a greater number of observations are needed to confirm the QTL associated with grazing distribution (SW15-015).
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".