Genome-wide association studies identify promising QTL for freezing tolerance in winter and early spring as a basis for in-depth genetic analysis and implementation in winter faba bean ( <i>Vicia faba</i> L.) breeding
Bibliographic record
Abstract
Abstract Interest in faba bean as a locally adapted high-protein grain legume crop has increased in Europe over the past decade. Winter faba bean, which can make use of soil moisture from autumn to spring and partially escape summer droughts, exhibit greater yield potential than the spring-type. However, due to insufficient winter hardiness, winterkill is a major constraint that prevents large-scale production of current winter-type cultivars in Central and Northern Europe. Here, we extend the understanding of freezing tolerance, the main component trait of winter hardiness, during winter and against late-frost in early spring and define genomic target regions for marker-assisted selection. Comparative analysis of genome-wide association studies revealed 13 treatment-specific major QTLs with partially pleiotropic effect on four freezing tolerance related traits. In addition, we identified five treatment-unspecific pleiotropic QTLs, including two major freezing tolerance loci on chromosomes 1 and 5. Our results thus indicate both a distinct and common genetic control of tolerance to winter- and late-frost in winter faba bean. In combination with the promising prediction abilities obtained from marker score-based prediction, our work highlights the potential for marker-assisted and genomic selection toward improved freezing tolerance and winter hardiness in winter faba bean breeding programs.
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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 | 0.000 |
| 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".