Molecular Breeding of Selçuklu-97 Durum Wheat Cultivar for Some Genes Affecting Pasta Quality
Bibliographic record
Abstract
The most efficient way of producing and supplying proper raw material desired by the pasta industry is the development of high quality durum wheat varieties. Therefore, quality of Turkish durum wheat varieties should be improved using modern breeding methods without adversely affecting their yields.In this study, important genes (y-gliadin 45 and LMW-2 glutenin) affecting the quality of pasta products were transferred to a Turkish durum wheat variety, Selçuklu-97, in a backcross breeding method in combination with marker assisted selection (MAS). A Canadian durum wheat cultivar with high quality, Kyle, was used as the donor parent. Each of F1 and backcross (BC) plants was backcrossed four times to the recurrent parent and in all of the generations, backcrossed plants carrying the targeted QTLs were selected by the MAS. The MAS method was employed in combination with embryo culture and rapid plant growth in a controlled greenhouse conditions. In identifying and transferring processes of the gene regions, molecular DNA markers (SSR, STS and GAG) were employed with A-PAGE and SDS-PAGE methods. A-PAGE was used for selection of y-gliadin 45, SDS-PAGE for selection of LMW-2 glutenins, and four SSR primers (Xgwm550, Xgwm608, Stm553actc, Stm542acag), one STS primer and one PCR primer linked to Gli-B1 and Glu-B3 loci for selection of y-gliadin 45 and LMW-2 glutenins either all together or alternatively. As a result, the study was completed in three years instead of six years required in a classical backcross breeding study, meaning about 50 % time saving, and obtained a high quality candidate variety
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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) | 0.001 | 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".