A High Density Genetic Map Derived from RAD Sequencing and Its Application in QTL Analysis of Yield-Related Traits in Vigna unguiculata
Bibliographic record
Abstract
Cowpea [Vigna unguiculata（L.）Walp.], spread widely in the semi-arid tropics, is an annual legume of economic importance. However, high-density genetic maps of cowpea are still in lack. Here, we identified 34,868 SNPs (single nucleotide polymorphisms) that were evenly distributed in the cowpea genome based on the RAD sequencing (restriction-site associated DNA sequencing) technique. Of these SNPs, 17,996 reliable SNPs were allotted to 11 consensus linkage groups (LGs) and were used for genotyping. The length of the genetic map was 1,194.25 cM in total with a mean distance of 0.066 cM/interval locus. The map quality and synteny were compared with the common bean (Phaseolus vulgaris) and the previous cowpea SNP genetic map. Using this map and the F2:3 population, combined with the CIM (composite interval mapping) method, eleven QTLs of yield-related trait were detected on seven LGs (LG4, 5, 6, 7, 9, 10 and 11) in cowpea. These QTLs explained 0.05~17.32% of the total phenotypic variation. Among these, four QTLs were for PL (pod length), four QTLs for TGW (thousand-grain weight), two QTLs for GN (grain number per pod), and one QTL for CL (carpopodium length). Our results will provide a foundation for understanding genes in the cowpea and genus Vigna.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".