Molecular mapping of QTLs for resistance to Fusarium wilt (race 1) and Ascochyta blight in chickpea (Cicer arietinum L.)
Bibliographic record
Abstract
Abstract Fusarium wilt (FW) and Ascochyta blight (AB) are two important diseases of chickpea which cause 100 % yield losses under favorable conditions. With an objective to validate and/or to identify novel quantitative trait loci (QTLs) for resistance to race 1 of FW caused by Fusarium oxysporum f. sp. ciceris and AB caused by Ascochyta rabiei in chickpea, two new mapping populations (F2:3) namely ‘C 214’ (FW susceptible) × ‘WR 315’ (FW resistant) and ‘C 214’ (AB susceptible) × ‘ILC 3279’ (AB resistant) were developed. After screening 371 SSR markers on parental lines and genotyping the mapping populations with polymorphic markers, two new genetic maps comprising 57 (C 214 × WR 315) and 58 (C 214 × ILC 3279) loci were developed. Analysis of genotyping data together with phenotyping data collected on mapping population for resistance to FW in field conditions identified two novel QTLs which explained 10.4–18.8 % of phenotypic variation. Similarly, analysis of phenotyping data for resistance to seedling resistance and adult plant resistance for AB under controlled and field conditions together with genotyping data identified a total of 6 QTLs explaining up to 31.9 % of phenotypic variation. One major QTL, explaining 31.9 % phenotypic variation for AB resistance was identified in both field and controlled conditions and was also reported from different resistant lines in many earlier studies. This major QTL for AB resistance and two novel QTLs identified for FW resistance are the most promising QTLs for molecular breeding separately or pyramiding for resistance to FW and AB for chickpea improvement.
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 | 0.000 |
| 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 | 0.000 | 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".