Mapping QTL for Individual and Total Isoflavone Content in Soybean Seeds
Bibliographic record
Abstract
Dietary intake of isoflavones has been shown to reduce the risk of several major diseases in humans. Therefore, breeding soybean [ Glycine max (L.) Merrill] seeds with desirable isoflavone content would be beneficial to the food and health industries, but the environmental sensitivity of the trait complicates phenotypic selection. The objective of this study was to identify quantitative trait loci (QTL) and epistatic interactions associated with isoflavone contents in soybean seeds. A population of 207 F 4:6 recombinant inbred lines (RILs) was produced from the cross ‘AC756’ × ‘RCAT Angora’. The population was phenotyped at two locations in Ontario, Canada, and genotyped by means of 99 polymorphic SSR markers. A significant genotype × environment interaction was found. Seventeen QTLs were detected ( P < 0.01) by single‐factor ANOVA. Individual loci explained up to 10.5% ( P < 0.0001) of the phenotypic variation. Interval mapping and composite interval mapping identified nine genomic regions (LGs A1, C2, D1a, F, G, H, J, K, and M) associated with isoflavone contents. Some QTL associated with agronomic or seed quality traits mapped to the same regions as those for individual isoflavone contents on LGs A1, C2, F, J, K, M, and N. Twenty‐three epistatic interactions were detected for isoflavones. Multiple locus models explained up to 25.0% ( P < 0.0001) of the phenotypic variation without epistasis and up to 35.8% ( P < 0.0001) with it. The QTL identified in this study could be useful for developing soybean varieties with desirable isoflavone content in the seed through marker‐assisted selection (MAS).
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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".