Accuracy of genomic prediction for seed oil concentration in high‐oleic soybean populations using a low‐density marker panel
Bibliographic record
Abstract
Abstract Insoybean [Glycine max (L.) Merr.], seed oil concentration is a complex quantitative trait, and genomic selection (GS) has been shown to be a valuable tool for performing selection on such traits. The objectives of this study were to evaluate multiple GS models for seed oil concentration using a low‐density marker panel in four biparental soybean populations and to assess predictive ability of the models using six unique training populations (TPs). Individuals were grown as BC1F4:F5 progeny rows in 2014. Genomic estimated breeding values (GEBVs) were calculated for each genotype within a population using genomic best linear unbiased predictor (GBLUP), BayesA, and BayesB models in a biparental specific context. In 2015, 60 individuals from each population were randomly selected and grown at six locations with two replications each to generate a “true” phenotypic value for each genotype. Prediction accuracies for each estimation set were generated by correlating the GEBVs with the “true” phenotypic value. Across all populations, the GS prediction accuracy was greatest using GBLUP; however, no GS prediction model showed a significant advantage in accuracy over the phenotypic values. Generally, TPs consisting of more individuals had higher prediction accuracies; however, variations were observed across populations and models. The results show that GS in a biparental context and with low marker densities can be a valuable tool for breeders focused on making gains for oil concentration; however, consideration must be given as to how to apply these methods to each situation.
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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 | 0.003 |
| 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 | 0.001 | 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 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".