Dissection of a novel major stable QTL on chromosome 7D for grain hardness and its breeding value estimation in bread wheat
Bibliographic record
Abstract
Grain hardness (Gh) is important for wheat processing and end-product quality. Puroindolines polymorphism explains over 60% of Gh variation and the novel genetic factors remain to be exploited. In this study, a total of 153 quantitative trait loci (QTLs), clustered into 12 genomic intervals (C1-C12), for 13 quality-related traits were identified using a recombinant inbred line population derived from the cross of Zhongkemai138 (ZKM138) and Chuanmai44 (CM44). Among them, C7 (harboring eight QTLs for different quality-related traits) and C8 (mainly harboring QGh.cib-5D.1 for Gh) were attributed to the famous genes, Rht-D1 and Pina, respectively, indicating that the correlation of involved traits was supported by the pleotropic or linked genes. Notably, a novel major stable QTL for Gh was detected in the C12, QGh.cib-7D, with ZKM138-derived allele increasing grain hardness, which was simultaneously mapped by the BSE-Seq method. The geographic pattern and transmissibility of this locus revealed that the increasing-Gh allele is highly frequently present in 85.79% of 373 worldwide wheat varieties and presented 99.31% transmissibility in 144 ZKM138-derivatives, indicating the non-negative effect on yield performance and that its indirect passive selection has happened during the actual breeding process. Thus, the contribution of this new Gh-related locus was highlighted in consideration of improving the efficiency and accuracy of the soft/hard material selection in the molecular marker-assisted process. Further, TraesCS7D02G099400, TraesCS7D02G098000, and TraesCS7D02G099500 were initially deduced to be the most potential candidate genes of QGh.cib-7D. Collectively, this study provided valuable information of elucidating the genetic architecture of Gh for wheat quality improvement.
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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".