QTL mapping of seed-quality traits in sunflower recombinant inbred lines under different water regimes
Bibliographic record
Abstract
The objectives of the present research were to determine the effects of water stress on seed-quality traits and to map QTLs controlling the studied traits under two different water treatments in a population of sunflower recombinant inbred lines (RILs). Two experiments were conducted in greenhouse and field conditions, each with well-watered and water-stressed treatments. The experiments consisted of a split-plot design (water treatment and RIL) with three blocks. Analyses of variance showed significant variation among genotypes, and a water treatment x genotype interaction was also observed for most of the traits. Two to 15 QTLs were found, depending on trait and growth conditions, and the percentage of phenotypic variance explained by the QTLs ranged from 5% to 31%. Several QTLs for oil content overlapped with QTLs for palmitic and stearic acid contents in all four conditions. An overlapping region on linkage group 3 (QTLs 2.OC.3.1 and 4.SA.3.1) was linked to an SSR marker (ORS657). A principal component analysis was performed on four fatty acid traits. Two principal components, P1 and P2, were used for QTL analysis. This method improved the ability to identify chromosomal regions affecting the fatty acids. We also detected the principal-component QTLs that did not overlap with the fatty acid QTLs. The results highlight genomic regions of interest in marker-based breeding programmes for increasing oil content in sunflower.
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 | 0.001 | 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".