Analysis of single nucleotide polymorphism and simple sequence repeat markers for the estimates of genetic diversity by using two oilseed <i>Brassica napus</i> populations carrying genome contents of <i>Brassica oleracea</i>
Bibliographic record
Abstract
Reliable estimates of genetic diversity among the accessions in a breeding population is important knowledge for use in breeding. Among the different types of molecular markers, single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) are largely used by breeders; however, our knowledge of the reliability of the estimates of genetic diversity based on these two types of markers in multiple populations is limited. In this study, a doubled haploid (DH) and an inbred population developed from Brassica napus × Brassica oleracea interspecific crosses were used for comparative analysis of these two types of markers. The estimates based on SNP and SSR markers showed a stronger correlation in the inbred population which was expected to carry greater genetic diversity as compared to the DH population. This inference was also evident from the analysis of different diversity groups (least, intermediate, and most similar) of these two populations for significant difference between the groups for six agronomic and seed quality traits, where this analysis failed to differentiate the diversity groups of the DH population for any of the traits. However, both marker types could differentiate the diversity groups of the inbred population for several traits. Furthermore, the six sub-populations of the inbred population could also be differentiated by both marker types. Thus, the results demonstrate the greater utility of the SSR and SNP markers in a genetically diverse population. This knowledge can be used while grouping a breeding population for diversity groups; however, caution needs to be taken while using the markers in a genetically narrow population.
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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) | 0.001 | 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".