QTL analysis of soybean oil content under 17 environments
Bibliographic record
Abstract
Qi, Z., Han, X., Hou, M., Xin, D., Wang, Z., Zhu, R., Hu, Z., Jiang, H., Li, C., Liu, C., Hu, G. and Chen, Q. 2014. QTL analysis of soybean oil content under 17 environments. Can. J. Plant Sci. 94: 245–261. Soybean oil content is a key trait driver of successful soybean quality. Due to its complex nature, less stable quantitative trait loci (QTL) are known. The goal of this study was to identify important and stable QTL affecting soybean oil content using recombination inbred lines (RILs) derived from a cross between Charleston and Dongnong594. The plant materials were planted in three environments across 9 yr in China. The genetic effects were then partitioned into additive main effects (A), epistatic main effects (AA) and their environment interaction effects (AE and AAE) by using composite interval mapping, multiple interval mapping and composite interval mapping in a mixed linear model. Fifty-six QTL were identified on 15 of 20 soybean chromosomes excluding LG C1, D2, E, M and O by composite interval mapping and multiple interval mapping methods. Seven oil content QTL detected on LG A1, 1 on LG A2, 5 on LG B1, 4 on LG B2, 8 on LG C2, 11 on LG D1a, 2 on LG D1b, 4 on LG F, 5 on LG G, 2 on LG H, 1 on LG I, 1 on LG J, 1 on LG K, 2 on LG L and 2 on LG N. Eight QTL showed a good stability across 17 environments. The additive main-effect QTL contributed more phenotypic variation than the epistasis and environmental interaction. This indicated that it is feasible to improve soybean oil content by marker-assisted selection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".