Genomic prediction models based on a large-scale recombinant population allow quick breeding of high-yield rice
Bibliographic record
Abstract
Abstract Rapid development of cultivars with optimal genome-wide allele combinations is essential for addressing agricultural challenges. However, constructing desired genotypes through conventional cross-breeding requires many generations, calling for a more efficient methodology. Here, we present a rapid breeding strategy that combines a large-scale recombinant population as starting material with interpretable genomic prediction models trained on that population. This approach enables efficient construction of target genotypes optimized for multiple traits in cultivars. To validate our strategy in rice ( Oryza sativa ), we established a nested association mapping population of 2,787 recombinant inbred lines derived from an elite cultivar ’Hitomebore’ and 19 diverse donors. We built highly accurate genomic prediction models using this population. We then used the models to estimate haplotype-specific effects and account for trade-offs among yield-related traits, and selected optimal lines and designed breeding schemes. Crossbreeding based on these schemes produced rice lines with the target genotypes for multiple yield-related traits, supporting the predicted effects and validating the effectiveness of our strategy. This genomic breeding approach provides a general framework for rapidly breeding cultivars able to meet the challenges posed by a changing environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".