Molecular Marker Based Design for Breeding Wheat Lines with Multiple Resistance and Superior Quality
Bibliographic record
Abstract
There has not been a major wheat stem rust epidemic worldwide since the 1970s, but the emergence of race TTKSK of Puccinia graminis f. sp. tritici in 1998 presented a great threat to the world wheat production. Single disease-resistance genes are usually effective for only several years before the pathogen changes genetically to overcome the resistance. Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is one of the most common and persistent wheat diseases worldwide. The development of varieties with multiple resistance is the most economical and effective strategy for preventing stripe rust and stem rust, the two main rust diseases constraining wheat production. Plateau 448 has been widely used in the spring wheat growing region in northwest China, but it has become susceptible to stripe rust and is susceptible to TTKSK. To produce more durable resistance to race TTKSK as well as to stripe rust, four stem rust resistance genes (Sr33, Sr36, Sr-Cad, and Sr43) and three stripe rust resistance genes (Yr5, Yr18, and Yr26) were simultaneously introgressed into Plateau 448 to improve its stem rust (Ug99) and stripe rust resistance using a marker-assisted backcrossing strategy combined with phenotypic selection. We obtained 131 BC1F5 lines that pyramided two to four Ug99 resistance genes and one to two Pst resistance genes simultaneously. Thirteen of these lines were selected for their TTKSK resistance, and all of them exhibited near immunity or high resistance to TTKSK. Among the 131 pyramided lines, 95 showed high resistance to mixed Pst races. Nine lines exhibited not only high resistance to TTKSK and Pst but also better agronomic traits and high-molecular-weight glutenin subunit compositions than Plateau 448.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".