Evaluation of genetic diversity and relatedness among apple cultivars using RAPD and SSR markers
Bibliographic record
Abstract
Twenty phenotypically different apple cultivars from the collection of Department of Fruit Science, YS Parmar University of Horticulture and Forestry, Solan, India, were analysed using 28 RAPD and 5 SSR primers to establish their genetic diversity, relatedness and verify parentage. The primer study revealed higher level of genetic diversity among the cultivars. The level of polymorphism across twenty cultivars was 65.6% and 71.4% from RAPD and SSR markers respectively. Primers OPA-16(RAPD), IISRS-3 and IISRS-18(SSR) were more informative due to the highest Polymorphism Information Content (PIC) value. Three RAPD primers were able to distinguish the cultivars. RAPD and SSR revealed different genetic similarities among apple cultivars. The highest similarity was detected between ‘Fuji’ and ‘Fuji Rekaki’ (0.859) followed by ‘Thanedar early flowering’ and ‘Fuji’ by RAPD while between ‘Galaxy’ and ‘Jonadel’ (1.000) followed by ‘Neomi’ with ‘Arlet’, ‘Fuji’ and ‘Royal Gala’; ‘Fuji Rekaki’ with ‘Thanedar early flowering’ and ‘Reinette-Du-Canada’ by SSR. The dendrograms from RAPD and SSR data were generated using UPGMA method which revealed four and five clusters respectively and showed that most of the cultivars clustered in accordance with the recorded pedigree information and polymorphism found with SSR primers was higher as compared to RAPD.
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 | 0.001 |
| 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".