Pyramiding of Genes/QTL for Resistance Against Three Rusts, High Grain Protein Content and Pre‐Harvest Sprouting Tolerance in Wheat ( <scp> <i>Triticum aestivum</i> </scp> L.) Using Marker‐Assisted Selection
Bibliographic record
Abstract
ABSTRACT In bread wheat, marker‐assisted selection (MAS) was employed, utilizing two parental lines that were previously developed by us through a combination of MAS and phenotypic selection. These parents carried improved rust resistance and some grain quality traits. SSR, SCAR and SNP markers (KASP assay) were used for 10 genes and also for a QTL for pre‐harvest sprouting tolerance (PHST); foreground MAS was exercised in F 2 , F 3 and F 4 generations leading to production of 15 improved lines. Eleven of these lines each carried the following (1) nine genes for resistance to the three rusts ( Lr19/Sr25 + Lr34/Yr18/Sr57 + Yr10 + Lr24/Sr24 + Yr36 ), (ii) a gene for high GPC ( Gpc‐B1 ) and (iii) a QTL for tolerance to PHS ( Qphs.dpivic‐4A.2 ) associated with amber grain colour. However, each of the remaining four lines possessed all the above genes/QTL, except Yr10 . Under artificial epiphytotic conditions, the improved lines were tested against 15 different pathotypes of the three rusts and were found to be resistant against all the pathotypes, barring some pathotypes for stripe rust resulting into moderate resistance in the four lines which lacked Yr10 . A set of F 7:8 lines containing targeted genes for all these traits in homozygous condition were selected and evaluated in replicated trials. Following field‐based phenotypic selection and marker profiling in F 9 generation, five lines with high grain yield and grain protein content were selected. These lines carried all the 10 targeted genes and a QTL for PHS tolerance except that three of these lines lacked Yr10 gene. The selected lines have the potential for use in the development of new wheat cultivars or else as useful pre‐bred lines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".