Simulations of genomic selection accuracy and model updating across multiple breeding strategy scenarios in common bean
Bibliographic record
Abstract
Abstract Genomic selection predicts the breeding value of selection candidates according to genotypes that are estimated to have favorable effects based on a model. The effectiveness of genomic selection is strongly tied to its prediction accuracy. Previous studies have evaluated the accuracy of genomic selection using simulations. The aim of this study was to evaluate changes in accuracy of genomic selection based on many known QTLs identified in the literature and determine their relationship with true breeding values. Simulation results revealed that correlation-based prediction accuracies (also referred to as realized accuracy) fluctuate depending on trait genetic architecture, breeding strategy and the number of initial parents involved in the breeding program. Generally, maximum accuracies were achieved under a mass selection strategy followed by pedigree and single seed descent methods. Model updating benefitted some breeding strategies more than others (e.g., single seed descent vs mass selection). For low heritability traits (i.e., yield), conventional methods provided comparable rates of genetic gain, but genetic gain under genomic selection reached a plateau in a lower number of cycles.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".