Uncovering Key Genes Associated with the Short-Winged Trait in Faba Bean (Vicia faba L.) Through Re-Sequencing and Genome-Wide Association Studies (GWASs)
Bibliographic record
Abstract
Faba bean (Vicia faba L.) is a globally significant legume valued for its applications in food, vegetables, and green manure, yet its high outcrossing rate (30–80%) poses challenges for production development. A rare short-winged trait identified in Yunnan, China, offers promise for developing low-outcrossing varieties, reducing outcrossing rates to below 5%. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) analyses revealed that the epidermal cells of normal wing petals are conical, while those of short-wing petals are tubular. This study examined 200 F2 lines from crosses between ‘K0692’ (short-winged) and ‘Yundou 1183’, as well as ‘Yundoulvxin 1’ (short-winged) and ‘Yundou 1183’. The GWASs identified 10 SNP loci across chromosomes 2, 3, 4, and 5, with SNP_chr4::1013887633 explaining 22.20% of the wing trait variation. Key candidate genes were identified, such as VFH_III145120, which influences floral identity; and VFH_III149200, associated with epidermal differentiation. GO enrichment analysis demonstrated significant gene involvement in RNA localization, ribosome biogenesis, and preribosome metabolism, while KEGG analysis linked these genes to pathways in amino acid, nucleotide, and purine metabolism; ubiquitin-mediated proteolysis; and protein processing in the endoplasmic reticulum. These findings lay a foundation for breeding low-outcrossing faba bean varieties and enhancing sustainable faba bean cultivation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".