CRISPR/Cas9-Mediated Editing of <i>TaGW2</i> to Enhance Grain Size in Wheat
Bibliographic record
Abstract
Wheat ( Triticum aestivum ) is a major food crop in the world, and its grain weight is one of the important traits that determine its yield. The TaGW2 gene is widely considered to be a key negative regulator of wheat grain size. With the development of CRISPR/Cas9 gene editing technology, targeted modification of the TaGW2 gene has become an important molecular breeding strategy for improving wheat grain weight. In this study, the structural characteristics and expression patterns of the TaGW2 gene were systematically analyzed, an efficient CRISPR/Cas9 editing system was designed, mutant materials were constructed, and their grain phenotypes were deeply evaluated. The study showed that the TaGW2 knockout mutant showed significant improvements in grain length, grain width, and 1000-grain weight, and had no adverse effects on plant height and growth period. This study collected and summarized actual editing cases from multiple authoritative institutions such as the Chinese Academy of Agricultural Sciences, CSIRO in Australia, and Nagoya University in Japan, verifying the wide applicability and breeding potential of TaGW2 editing in different genetic backgrounds. In this study, CRISPR/Cas9 technology was used to precisely edit the wheat TaGW2 gene in order to enhance the length, width, and 1000-grain weight of the grain, thereby improving the yield potential of wheat.
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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 | 0.001 |
| 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".