Genomic Selection Accuracy for Grain Quality Traits in Biparental Wheat Populations
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Bibliographic record
Abstract
ABSTRACT Genomic selection (GS) is a promising tool for plant and animal breeding that uses genome‐wide molecular marker data to capture small and large effect quantitative trait loci and predict the genetic value of selection candidates. Genomic selection has been shown previously to have higher prediction accuracies than conventional marker‐assisted selection (MAS) for quantitative traits. In this study, we compared phenotypic and marker‐based prediction accuracy of genetic value for nine different grain quality traits within two biparental soft winter wheat ( Triticum aestivum L.) populations. We used a cross‐validation approach that trained and validated prediction accuracy across years to evaluate effects of model training population size, training population replication, and marker density. Results showed that prediction accuracy was significantly greater using GS versus MAS for all traits studied and that accuracy for GS reached a plateau at low marker densities (128–256).The average ratio of GS accuracy to phenotypic selection accuracy was 0.66, 0.54, and 0.42 for training population sizes of 96, 48, and 24, respectively. These results provide further empirical evidence that GS could produce greater genetic gain per unit time and cost than both phenotypic selection and conventional MAS in plant breeding with use of year‐round nurseries and inexpensive, high‐throughput genotyping technology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it