Assessment of Genetic Variation in Soybean (<i>Glycine max</i>) Accessions from International Gene Pools Using RAPD Markers: Comparison with the ISSR System
Bibliographic record
Abstract
Soybean (Glycine max) is one of the most important crops in the world in terms of total production and usage. It is also among the least diverse species. The main objectives of the present study were to 1) assess the level of genetic variation among soybean (G. max) accessions from different countries using Random Amplified Polymorphic DNA (RAPD) markers and 2) compare Inter Simple Sequence Repeats (ISSR) and RAPD marker systems in detecting polymorphic loci in soybeans (G. max). Genomic DNAs from 108 soybeans (G. max) accessions from 11 different gene pools were analyzed using several ISSR and RAPD primers. The average level of polymorphic loci detected with the RAPD primers was 35%. The soybean accessions from the China, Netherlands, and Canada gene pools were the least genetically variable with 25%, 26%, and 30% of polymorphic loci, respectively. Accessions from Hungary (43%) and France (48%) showed the highest level of polymorphism based on the RAPD analysis. Overall, RAPD data revealed that the accessions from different countries are closely related with 64% genetic distance values below 0.40. The levels of polymorphic loci detected with the RAPD and ISSR marker systems were in general moderate and similar even if they target different regions of the genome. A combination of different marker systems that include RAPD/ISSR, microsatellites (SSR), and SNPs should provide the most accurate information on genetic variation of soybean (G. max) accessions.
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 | 0.001 | 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) | 0.000 | 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".