Accounting for uncertainty in QTL location in marker‐assisted pre‐selection of young bulls prior to progeny test
Bibliographic record
Abstract
The objective of this study was to evaluate whether the efficacy of marker assisted selection (MAS) could be improved by considering a confidence interval (CI) of QTL position. Specifically, MAS was applied for within‐family selection in a stochastic simulation of a closed nucleus herd. The location and effect of the QTL were estimated by least squares interval mapping with a granddaughter design and marker information was then used in a top down scheme. Three approaches were used to select the best bull within full sibships of 3 or 40 bulls. All three were based on the probability of inheriting the favorable allele from the grandsire (PROB). The first method selected the sib with the highest PROB at the location with the highest F‐ratio (MAX). The other two approaches were based on sums of estimated regression coefficients weighted by PROB at each cM within a 95% CI based on either bootstrapping (BOOT) or approximate LOD scores (LOD). Accounting for CI increased the relative genetic gain in all scenarios. The average breeding value (BV) of the selected bulls was increased by 2.00, 2.60 and 2.59% when MAS was applied using MAX, BOOT and LOD, respectively, compared to random selection (h2=0.30). Selected bulls carried the correct allele in 63.0, 68.5, 67.6 and 50.1% of the cases for MAX, BOOT, LOD and random selection, respectively.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".