Simulations of genomic selection implementation pathways in common bean ( <i>Phaseolus vulgaris</i> L.) using parametric and nonparametric models
Bibliographic record
Abstract
We conducted simulations of common bean (Phaseolus vulgaris L.) breeding programs to better understand the interplay between different choices a breeder must make when launching a genomic selection (GS) pipeline. We complement preceding studies on optimizing model parameters and training set makeup by exploring the practical implementation of GS in a common bean breeding program aimed at increasing seed yield. We simulated 24 GS implementation pathways on (1) what generation to train a new prediction model, (2) what generation to select parents for the next cycle, (3) which generation to collect training data, and (4) whether to use a parametric (ridge regression best linear unbiased predictor) or a nonparametric model (artificial neural network) for estimating breeding values. We found that early generation parent selections (also called rapid-cycle GS) generally resulted in higher gain over three breeding cycles compared to late-generation parent selections. When implementing a new parametric genomic prediction model, training data should be as diverse as possible, while also matching testing data in terms of genetic makeup and allele frequency. Parametric models showed more consistent genomic estimated breeding value prediction accuracy, while nonparametric models fluctuated, showing both the highest and the lowest prediction accuracy across all pathways. Despite the trade-off between gains and genetic variance, nonparametric models showed greater balance of allelic diversity and gains. We observed that the key to sustained gains over time is the renewal of genetic variance. Our results indicate a potential for the use of nonparametric models, but more investigation will be required to stabilize their performance.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".