Multiplex‐genome‐editing based rapid directional improvement of complex traits in rice
Bibliographic record
Abstract
Although thousands of genes have been identified or cloned in rice (Oryza sativa) in the last two decades, the majority of them have only been separately characterized in specific varieties or single-gene modified backgrounds, thus limiting their practical application. We developed an optimized multiplex genome editing (MGE) toolbox that can efficiently assemble and stably express up to twelve sgRNA targets in a single plant expression vector. In this study, we established the MGE-based Rapid Directional Improvement (MRDI) strategy for directional improvement of complex agronomic traits in one small-scale rice transformation. This approach provides a rapid and practical procedure, encompassing sgRNA assembly, transgene-free screening and the creation of promising germplasm, by combining the precision of gene editing with phenotype-based field breeding. The MRDI strategy was used to generate the full diversity of twelve main agronomic genes in rice cultivar FXZ for the directional improvement of its growth duration and plant architecture. After applying the MRDI to FXZ, ideal plants with the desired traits of early heading date reduced plant height, and more effective panicles were generated without compromising yield, blast resistance and grain quality. Furthermore, the results of whole-genome sequencing (WGS), including the analysis of structural variations (SVs) and single nucleotide variations (SNVs) in the MGE plants, confirmed the high specificity and low frequency of unwanted mutations associated with this strategy. The MRDI breeding strategy would be a robust approach for exploring and applying crucial agronomic genes, as well as for generating novel elite germplasm in the future.
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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 | 0.000 | 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) | 0.000 | 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".