Context‐Specific Marker‐Assisted Selection for Improved Grain Yield in Elite Soybean Populations
Bibliographic record
Abstract
Despite the importance of grain yield potential to plant breeders and society in general, it has been difficult to identify grain yield quantitative trait loci (QTL) effective for marker‐assisted selection (MAS) across a wide range of genetic and/or environmental contexts. However, as genotyping becomes more cost effective, it might be feasible to use preliminary yield trials to model a target genotype within each context and immediately select the progeny that approach that target genotype in real time. In the present study, elite soybean cultivars with residual heterogeneity were leveraged as populations (the genetic context) to detect yield QTL within a limited set of environments (the environmental context), to model a target genotype, and to select subline haplotypes that comprised the target genotype. The yield potential of the selected subline haplotypes were then compared to their respective mother lines in highly replicated yield trials across multiple environments and years. Statistically significant yield gains of up to 5.8% were confirmed in some of the selected sublines, and two of the improved sublines were released as improved cultivars. This context‐specific MAS (CSM) approach might also be applicable to the more typical biparental and backcross populations commonly used in plant breeding programs. Factors that can affect the efficiency and applicability of CSM are discussed.
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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 | 0.000 |
| 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 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".