Mapping QTL Associated with Traits Affecting Grain Yield in Chickpea (<i>Cicer arietinum</i> L.) under Terminal Drought Stress
Bibliographic record
Abstract
The aim of this study was to evaluate a set of recombinant inbred lines (RILs) for agronomic and physiological traits under drought conditions and to locate quantitative trait loci (QTL) associated with them. This study used a RIL population derived from a cross between drought tolerant (ILC 588) and susceptible (ILC 3279) genotypes. The population consisting of 155 RILs was grown under drought conditions in the field at Tel Hadya, Syria, in 2006 and 2007 and at Breda, Syria, in 2007. A genetic map consisting of eight linkage groups was developed using 97 simple sequence repeat (SSR) markers. The results revealed that high harvest index (HI), early flowering, and early maturity were the important attributes contributing to higher grain yield under drought. Higher stomatal conductance (g s ) and cooler canopies (canopy temperature minus air temperature [Tc–Ta]) can also lead to better performance under drought conditions. Quantitative trait locus analysis identified 15 genomic regions significantly associated with various traits affecting drought tolerance in chickpea. Important QTL detected in this study included two QTL for HI explaining 38% of the total phenotypic variability of the trait, four QTL for flowering explaining 45%, and three QTL for maturity explaining 52% on a cumulative basis. Three QTL for g s and six QTL for Tc–Ta also detected explained 7 to 15% phenotypic variability individually. Two QTL (Q3‐1 and Q1‐1) on linkage group 3 (LG3) and LG1 showed effects on many traits related to drought. Hence, these regions can be further explored in future drought studies.
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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 | 0.000 | 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) | 0.000 | 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".