Genetic bottlenecks in Turkish okra germplasm and utility of iPBS retrotransposon markers for genetic diversity assessment
Bibliographic record
Abstract
Lack of requisite genetic variation in Turkish okra has necessitated the use of different types of markers for estimating the genetic diversity and identifying the source of variation. Transposable elements, present abundantly in plant genomes, generate genomic diversity through their replication and are thus an excellent source of molecular markers. We hypothesized that inter-primer binding site (iPBS)-retrotransposons could be the source of variation because of their genome plasticity nature. In the present study, genetic diversity of 66 okra landraces was analyzed using iPBS-retrotransposon markers. iPBS-retrotransposons detected 88 bands with 40.2% polymorphism and an average of 6.8 bands per primer. Gene diversity and Shannon's information index ranged from 0.01 to 0.13 and 0.02 to 0.21 for iPBS-retrotransposons and from 0.06 to 0.46 and 0.14 to 0.65 for simple sequence repeat (SSR) markers, respectively. Polymorphism information content value for retrotransposons varied between 0.12 and 0.99, while that for SSR was from 0.52 to 0.81. Neighbor joining analysis based on retrotransposons and SSRs divided all the accessions into four clusters; however, SSR markers were more efficient in clustering the landraces based on their origin. Using the STRUCTURE software for determining population structure, and two populations (at the number of hypothetical subpopulations, K = 2) were identified among the landraces. Low genetic diversity in Turkish okra highlights the need for the introduction of plants from countries with greater genetic diversity for these crops. This study also demonstrates the utility and role of iPBS-retrotransposons, a dominant and ubiquitous part of eukaryotic genomes, for diversity studies in okra.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".