An SSR-based linkage map of yardlong bean (<i>Vigna unguiculata</i>(L.) Walp. subsp.<i>unguiculata</i>Sesquipedalis Group) and QTL analysis of pod length
Bibliographic record
Abstract
Yardlong bean (Vigna unguiculata (L.) Walp. subsp. unguiculata Sesquipedalis Group) (2n = 2x = 22) is one of the most important vegetable legumes of Asia. The objectives of this study were to develop a genetic linkage map of yardlong bean using SSR makers from related Vigna species and to identify QTLs for pod length. The map was constructed from 226 simple sequence repeat (SSR) markers from cowpea (Vigna unguiculata (L.) Walp. subsp. unguiculata Unguiculata Group), azuki bean (Vigna angularis (Willd.) Ohwi & Ohashi), and mungbean (Vigna radiata (L.) Wilczek) in a BC(1)F(1) ((JP81610 × TVnu457) × JP81610) population derived from the cross between yardlong bean accession JP81610 and wild cowpea (Vigna unguiculata subsp. unguiculata var. spontanea) accession TVnu457. The markers were clustered into 11 linkage groups (LGs) spanning 852.4 cM in total length with a mean distance between adjacent markers of 3.96 cM. All markers on LG11 showed segregation distortion towards the homozygous yardlong bean JP81610 genotype. The markers on LG11 were also distorted in the rice bean (Vigna umbellata (Thunb.) Ohwi & Ohashi) map, suggesting the presence of common segregation distortion factors in Vigna species on this LG. One major and six minor QTLs were identified for pod length variation between yardlong bean and wild cowpea. Using flanking markers, six of the seven QTLs were confirmed in an F(2) population of JP81610 × TVnu457. The molecular linkage map developed and markers linked to pod length QTLs would be potentially useful for yardlong bean and cowpea breeding.
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 | 0.000 | 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) | 0.001 | 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".