Integration of simple sequence repeat (SSR) markers into a molecular linkage map of common bean (Phaseolus vulgaris L.)
Bibliographic record
Abstract
Microsatellite or simple sequence repeat (SSR) markers have been successfully used for genomic mapping, DNA fingerprinting, and marker-assisted selection in many plant species. Here we report the first successful assignment of 15 SSR markers to the Phaseolus vulgaris molecular linkage map. A total of 37 SSR primer pairs were developed and tested for amplification and product-length polymorphism with BAT93 and Jalo EEP558, the parental lines of an F7 recombinant inbred (RI) population previously used for the construction of a common bean molecular linkage map. Sixteen of the SSRs polymorphic to the parental lines were analyzed for segregation and 15 of them were assigned to seven different linkage groups, indicating a widespread distribution throughout the bean genome. Map positions for genes coding for DNAJ-like protein, pathogenesis-related protein 3, plastid-located glutamine synthetase, endochitinase, sn-glycerol-3 phosphate acyltransferase, NADP-dependent malic enzyme, and protein kinase were determined for the first time. Addition of three SSR loci to linkage group B4 brought two separated smaller linkage groups together to form a larger linkage group. Analysis of allele segregation in the F7 RI population revealed that all 16 SSRs segregated in the expected 1:1 ratio. These SSR markers were stable and easy to assay by polymerase chain reaction (PCR). They should be useful markers for genetic mapping, genotype identification, and marker-assisted selection of common beans.
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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) | 0.000 | 0.000 |
| 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".