Prospects for Cost‐Effective Genomic Selection via Accurate Within‐Family Imputation
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Bibliographic record
Abstract
Genomic selection has great potential to increase the efficiency of plant breeding, but its implementation is hindered by the high costs of collecting the necessary data. In this study we evaluated the potential of accurate within‐family imputation for enabling cost‐effective genomic selection. We have simulated a breeding program with inbred parents and their segregating progeny distributed among families, of which some were used as a training set and some were used as a prediction set. Parents were genotyped at high density (20,000 markers), while progeny were genotyped at high or low density (500, 200, 100, or 50 markers) and imputed. Low‐density markers were chosen to segregate within each family separately. The assumed low‐density genotyping costs accounted for this assumption. Six sets of scenarios were analyzed in which imputation was leveraged to maximize cost effectiveness of genomic selection by (i) decreasing the genotyping costs, (ii) increasing selection intensity by genotyping more individuals at fewer markers, or (iii) increasing prediction accuracy by genotyping more phenotyped individuals at fewer markers. The results show that, with a constant size of the training and prediction sets, the prediction accuracy was unimpaired when at least 200 low‐density markers were used. However, the return on investment was maximal (5.67 times that of the baseline scenario) when only 50 low‐density markers were used because that enabled maximal reduction in the genotyping costs and only minimal reduction in the prediction accuracy. Increasing either the training set or prediction set further increased the return on investment when imputed genotypes were used, but not when the true high‐density genotypes were used. The results show how plant breeding programs can implement genomic selection in a cost‐effective way.
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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