Development of pre-breeding diploid potato germplasm displaying wide phenotypic variations as induced by ethyl methane sulfonate mutagenesis
Bibliographic record
Abstract
Mutations are the key drivers for evolution and diversification in plants. In varietal selection, sources for variation are always sought as starting breeding materials. Thus, in the absence of desired natural variations in breeding populations, targeted or random mutagenesis is applied to induce variations. Cultivated potato (Solanum tuberosum L.) is autotetraploid crop species with a narrow and highly heterozygous genetic base, and the complexity of its genome makes its genetic studies more difficult. In the current study, induced mutagenesis was performed in diploid potato using ethyl methane sulfonate (EMS) to enlarge the genetic variability for its use as pre-breeding materials in both polyploid and diploid potato breeding. As starting materials, true potato seeds were treated with 1.2% EMS for 4–6 h along with untreated seeds as controls. A large variation in terms of germination rate, plant, flower, and tuber phenotype was observed in EMS-treated plants compared with their untreated counterparts. In particular, abnormal phenotypes including twisted stem, partial and (or) completely chlorotic leaves and stems, variations in stem colour and weak-stemmed plants with lateral growth habit as well as plants with determinate growth habit were observed along with normal plant characteristics. Moreover, variations in flower colour and tuber colour, shape, and size, as well as yield potential, were observed in EMS-treated lines. The reported phenotypic characterization of EMS mutagenized diploid potato collection is to our knowledge the first in its kind and represents a premium genetic resource for potato breeding programs and plant biologists for genes functional characterization in potato.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".