Genetic Study and QTL Mapping of Seed Glucosinolate Content in <i>Brassica rapa</i> L.
Bibliographic record
Abstract
ABSTRACT Self‐incompatibility in Brassica rapa L. is a major impediment to experimental studies on the genetic control of quantitative traits such as seed glucosinolate (GSL) content. In this paper, we report quantitative trait loci (QTL) mapping of total seed GSL content using a recombinant inbred line (RIL) population derived from two self‐compatible high‐ and low‐GSL B. rapa grown under different environmental conditions. Furthermore, quantitative genetic analysis using the parents and their F1, F2, B1 (F1 backcrossed to high‐parent P1), B2 (F1 backcrossed to low‐parent P2), and self‐pollinated progenies of B1 generation populations are also reported. Quantitative genetic analysis showed that additive genetic variance was consistently significant under different environments, while the dominance effect was significant under one growth condition. However, a simple additive‐dominance model was inadequate to explain the segregation variation among the generation means. Nonallelic interaction effects were important in the genetic control of GSL content in early generations where the levels of heterozygosity remained high. QTL mapping detected three loci at the linkage groups A2, A7, and A9 involved in the control of this trait. These QTL individually explained 5 to 22% of total phenotypic variation. The QTL on A9 was detected in all environments and explained 22%, the greatest amount of phenotypic variation. No additive × additive gene interaction was detected based on QTL analysis, and this also largely agreed with quantitative genetic analysis. Similarly, the number of loci detected based on QTL mapping also agree with the results obtained from quantitative genetic analysis.
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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 | 0.000 |
| 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".