Prioritization of Deleterious Mutations Improves Genomic Prediction and Increases the Rate of Genetic Gain in Common Bean ( <i>Phaseolus vulgaris L.</i> ), a Simulation Study
Bibliographic record
Abstract
Abstract The study of mutations is fundamental to understanding evolution, domestication, and genetics. Characterizing mutations has the potential to accelerate breeding programs through selection and purging of deleterious mutations (DelMut). Here, we investigated how predicting DelMut in breeding populations can improve genomic prediction (GP) and inform strategies to increase the rate of genetic gain. DelMut were annotated in three independent common bean populations using a previously developed random forest (RF) model incorporating phylogenetic and protein information. Deleterious scores from the RF model were mostly around 0.25, with the top 1% ( highly DelMut) of variants scoring between 0.78 – 0.82 among populations. All populations showed variation in the number of highly DelMut per line (max. 13 – 197) and in genetic load. We assessed the impact of incorporating a priori information for variant prioritization and weighting based on predicted deleteriousness in GP models for yield and flowering time. Stochastic simulations were conducted to evaluate how different mating schemes based variable numbers of DelMut per parent affect genetic gain. Variants with higher predicted scores had significantly different effect distributions compared to random or lower-scored markers. Yield predictions were 4.47–12.3% more accurate when markers were weighted by effect and deleterious score; no consistent improvement was observed for flowering time. Simulated breeding cycles showed that selecting parents with fewer highly DelMut consistently increases the rate of genetic gain. These results highlight the potential of DelMut information for variant prioritization and the optimization of common bean breeding programs. The approaches we developed can be assessed in other species to improve the efficacy of crop improvement. Key messages - Predicted deleterious mutations have different distributions of effects based on population composition. - Variant prioritization and differential weighing of markers based on effects and deleterious scores can improve the prediction of yield. - Favoring mating schemes between parents with fewer highly deleterious mutations can increase the rate of genetic gain.
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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 | 0.002 |
| 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 | 0.000 |
| 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".