Genetic diversity in three groups of barley germplasm assessed by simple sequence repeats
Bibliographic record
Abstract
Genetic diversity can be measured by several criteria, including phenotype, pedigree, allelic diversity at marker loci, and allelic diversity at loci controlling phenotypes of interest. Abundance, high level of polymorphism, and ease of genotyping make simple sequence repeats (SSRs) an excellent molecular marker system for genetics diversity analyses. In this study, we used a set of mapped SSRs to survey three representative groups of barley germplasm: a sample of crop progenitor (Hordeum vulgare subsp. spontaneum) accessions, a group of mapping population parents, and a group of varieties and elite breeding lines. The objectives were to determine (i) how informative SSRs are in these three sets of barley germplasm resources and (ii) the utility of SSRs in classifying barley germplasm. A total of 687 alleles were identified at 42 SSR loci in 147 genotypes. The number of alleles per locus ranged from 4 to 31, with an average of 16.3. Crop progenitors averaged 10.3 alleles per SSR locus, mapping population parents 8.3 alleles per SSR locus, and elite breeding lines 5.8 alleles per SSR locus. There were many exclusive (unique) alleles. The polymorphism information content values for the SSRs ranged from 0.08 to 0.94. The cluster analysis indicates a high level of diversity within the crop progenitors accessions and within the mapping population parents. It also shows a lower level of diversity within the elite breeding germplasm. Our results demonstrate that this set of SSRs was highly informative and was useful in generating a meaningful classification of the germplasm that we sampled. Our long-term goal is to determine the utility of molecular marker diversity as a tool for gene discovery and efficient use of germplasm.
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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 | 0.001 |
| 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".