Simulations of multiple breeding strategy scenarios in common bean for assessing genomic selection accuracy and model updating
Bibliographic record
Abstract
Genomic prediction allows breeders to make selections based on the genomic estimated breeding value (GEBV) of selection candidates. GEBVs are assigned using a prediction model trained with genotypes and phenotypes from a training population. For this reason, the effectiveness of genomic selection is strongly tied to the prediction accuracy of the model used to estimate breeding values and the training population used to inform the model. The aim of this study was to evaluate the accuracy of the ridge regression best linear unbiased prediction (rrBLUP) model across different traits, parent population sizes, and breeding strategies when estimating breeding values in Phaseolus vulgaris. The model was trained on a simulated population genotyped for 1010 SNP markers including 38 known QTLs identified in the literature (Lin, 2022). Simulation results revealed that realized accuracies fluctuate depending on the factors investigated: trait genetic architecture, breeding strategy, and the number of initial parents involved in the breeding program. Trait architecture and breeding strategy appeared to have a larger impact on accuracy than the initial number of parents. Generally, maximum accuracies were achieved under a mass selection strategy followed by pedigree and single-seed descent methods. This study also investigated model updating, which involves re-training the prediction model with a more relevant set of genotypes and phenotypes. While it has been repeatedly shown that model updating generally improves prediction accuracy, it benefitted some breeding strategies more than others. For low heritability traits (e.g., yield) conventional phenotype-based selection methods showed consistent rates of genetic gain, but genetic gain under genomic selection reached a plateau in after fewer 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".