Overcoming breeding constraints in polyploid oat from evolutionary insights
Bibliographic record
Abstract
Polyploidy provides adaptive advantages in plants by buffering deleterious mutations1,2. While polyploidization can enhance agronomic traits such as increased biomass, known as the gigas effect (1,2,3,4), increasing genetic gain in polyploid crops remains a critical but difficult goal due to the difficulty in dissecting complex trait inheritance (5,6). Here we present chromosome-scale genome assemblies for 26 Avena taxa, spanning diploid, tetraploid, and hexaploid lineages. We traced four independent polyploidization events across the genus, including the formation of A. agadiriana as an allotetraploid (AgAgAgʹAgʹ), and revealed a reticulate evolutionary history shaped by gene flow involving four subgenomes (A, B, C, D), for example, the hybrid speciation of A. hirtula. Transcriptomic analysis of 286 samples across 11 tissues, combined with deleterious mutation analysis from a cultivated population of 112 accessions, showed that polyploidization led to widespread functional redundancy among homoeologs, supporting a genome-wide buffering effect. However, derived allele frequency analysis revealed that, while disrupting functional genes may yield desirable traits, the buffering effect impedes the fixation of beneficial loss-of-function mutations and thereby limits breeding efficiency. Based on these integrative analyses, we propose breeding strategies to circumvent these limitations by targeting beneficial loss-of-function alleles within the complex polyploid background of oat. Our study highlights broader challenges in the improvement of polyploid crops and provides a foundation for future breeding strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".