Identification of QTL underlying soybean agglutinin content in soybean seeds and analysis for epistatic effects among multiple genetic backgrounds and environments
Bibliographic record
Abstract
Soybean agglutinin (SBA), a major anti-nutritional factor in soybeans, seriously affects the safety of and effects on feed animals. Soybean varieties with lower SBA contents could play a positive role in the promotion of the fodder industry. In the present study, one male parent, Hefeng 45, was used in three recombinant I bred line (RIL) populations to detect quantitative trait loci (QTL) associated with SBA content across three different environments. As a result, a total of six, six, and four QTL related to SBA content were identified in the groups Hefeng 45 × Zhongdou 27 (HZ), Hefeng 45 × Dongnong 48 (HD), and Hefeng 45 × Taipingchuan Blacksoy (HT), respectively. Most of the QTL underlying SBA explained less than 10% of the phenotypic variation, but some major QTL with higher additive effects have stable expressions across different environments and RIL populations. On the contrary, many QTL dependent on the environment or RIL populations showed mainly weak effects, and gene × environment interaction occurred in the opposite direction to additive effects and (or) epistasis effects. Two (SbaHZC1-1 and SbaHZD1b-1, linked to Satt139 and Satt189, respectively) and one (SbaHDD1a-1, linked to Satt402) QTL for SBA were identified in HZ and HD populations, respectively, across three environments. Those QTL (Satt139 and Satt189) might have potential in the application of marker-assisted selection for low agglutinin content soybean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".