Genetic structure of wild emmer wheat populations as reflected by transcribed versus anonymous SSR markers
Bibliographic record
Abstract
Simple sequence repeat (SSR) markers have become a major tool in population genetic analyses. The anonymous genomic SSRs (gSSRs) have been recently supplemented with expressed sequence tag (EST) derived SSRs (eSSRs), which represent the transcribed regions of the genome. In the present study, we used 8 populations of wild emmer wheat (Triticum turgidum subsp. dicoccoides) to compare the usefulness of the two types of SSR markers in assessing allelic diversity and population structure. gSSRs revealed significantly higher diversity than eSSRs in terms of average number of alleles (14.92 vs. 7.4, respectively), polymorphic information content (0.87 vs. 0.68, respectively), and gene diversity (He; 0.55 vs. 0.38, respectively). Despite the overall differences in the level of diversity, Mantel tests for correlations between eSSR and gSSR pairwise genetic distances were found to be significant for each population as well as for all accessions jointly (RM=0.54, p=0.01). Various genetic structure analyses (AMOVA, PCoA, STRUCTURE, unrooted UPGMA tree) revealed a better capacity of eSSRs to distinguish between populations, while gSSRs showed a higher proportion of intrapopulation (among accessions) diversity. We conclude that eSSR and gSSR markers should be employed in conjunction to obtain a high inter- and intra-specific (or inter- and intra-varietal) distinctness.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".