Genetic and environmental analyses of tocopherols in soybean seeds
Bibliographic record
Abstract
Soybeans contain considerable amounts of tocopherols, which have a high market value. Therefore, the development of high tocopherol soybean lines is desired. Tocopherol synthesis in soybean is environmentally influenced, which makes phenotypic selections inefficient. The objectives of this thesis were: (1) to evaluate tocopherol/fatty acid accumulation of soybean lines under controlled environments, (2) to investigate the impact of storage on tocopherol concentrations in soybeans, (3) to map quantitative trait loci (QTL) for tocopherols/fatty acids in soybeans. Results indicated that prolonged seed storage caused major tocopherol losses, and storage temperatures of 20°C and -20°C only allowed for moderate short-term tocopherol preservation. Unique cultivar responses to storage suggest genetic causes for breakdown. Fast soybean processing is recommended to prevent major tocopherol losses from seed. Elevated growing temperatures enhanced tocopherol concentration, which was attributed to [alpha]-tocopherol increases and consequent reductions of [gamma]- and [delta]-tocopherol. Positive correlations were found between oleic acid (18:1) and tocopherols, whereas linoleic (18:2) and linolenic (18:3) acid were negatively correlated with growing temperature, and tocopherols. Soybeans low in 18:2 and 18:3 contained more tocopherols. QTL analysis was performed based on field data from three Ontario locations in 2004 and 2005, using a RIL population with 79 RIL derived from OAC Shire x OAC Bayfield. Tocopherol QTL were identified on linkage groups A2, C2, D1a, D1b, and F. Most tocopherol QTL mapped to similar genomic regions as those of fatty acids, which suggests a potential for targeting them for a concurrent modification of fatty acids and tocopherol. This is the first report of tocopherol inheritance in soybean seed, which, combined with identified QTL, could lead to the development of novel tocopherol profiles in soybean breeding lines.
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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 | 0.001 | 0.001 |
| 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".