Effect of Genotype, Environment, and Genotype × Environment Interaction on Tocopherol Accumulation in Soybean Seed
Bibliographic record
Abstract
ABSTRACT Soybean seeds [Glycine max (L.) Merrill] are a major source of tocopherols that provide many human health benefits including decrease in lung cancer risk and osteoporosis. The objectives of this study were: (i) to determine the impact of genotype, environment, and genotype × environment on soybean seed tocopherols and (ii) to evaluate relationships between agronomic traits and tocopherols. Seventy‐nine recombinant inbred lines (RILs) from the cross OAC Bayfield × OAC Shire were grown in three field locations in southern Ontario, Canada, in 2009 and 2010. Agronomic and seed quality data were collected in each location. Significant differences (P < 0.001) for the α‐, γ‐, δ‐, and total tocopherol concentrations were found in the seed of RILs using combined ANOVA for 2009 and 2010. Of the two parental lines, OAC Bayfield accumulated more total tocopherol at most environments. Genotype, location, and year differences were significant as well as genotype × location, location × year, and line × year interactions. Broad‐sense heritability estimates were 0.38 for α‐, 0.47 for γ‐, and 0.35 for δ‐tocopherol. None of the agronomic traits were consistently correlated with any of the tocopherol components; however, oil and protein concentration were correlated to some tocopherols in some of the environments. The wide range in values for α‐, γ‐, δ‐, and total tocopherols among the RIL population that exceeded both parents provided evidence for transgressive segregation. Our results suggest that soybean producers should take locations and genotypes into account when growing soybean for enhanced tocopherol production.
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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 | 0.000 | 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) | 0.001 | 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".