Mapping of genomic regions associated with agronomic traits and resistance to diseases in Canadian spring wheat
Bibliographic record
Abstract
Wheat breeders, in addition to phenotypic selection, employ molecular markers in their programs for different purposes, including parental selection, quality control, analysis of advanced lines (cultivars), on genetic purity and identity, and for markers assisted selection. In the first study of this thesis we evaluated 158 recombinant inbred lines (RILs) population for flowering, maturity, plant height and grain yield under field conditions. With a subset of 1809 single nucleotide polymorphisms (SNPs) and 2 functional markers (Ppd-D1 and Rht-D1) we identified a total of 19 quantitative trait loci (QTLs) associated with flowering time under greenhouse (5) and field (6) conditions, maturity (5), grain yield (2) and plant height (1). These QTLs explained between 6.3 and 37.8% of the phenotypic variation. Only the QTLs on both 2D chromosome (adjacent to Ppd-D1) and 4D chromosome (adjacent Rht-D1) had major effects and, respectively reduced flowering and maturity time up to 5 days with a yield penalty of 436 kg ha-1 and reduced plant height by 13 cm, but increased maturity by 33 degree days. In the second study, we used genome-wide association analysis (GWAS) to identify markers associated with the wheat diseases leaf rust, stripe rust, tan spot, common bunt and three host selective toxins (HST) from Pyrenophora tritici-repentis (Ptr ToxA, B and C). We were able to identify 94 markers associated with all traits except Ptr ToxC sensitivity. Two major effect genomic regions on 5B and 1A were associated with Ptr ToxA sensitivity, of which the former coincided with the Tsn1 gene. For Ptr ToxB, two other major effect regions on chromosomes 2B and 5B. The genomic regions associated with common bunt mapped on chromosomes 2B, 4B and 7A, while those associated with leaf rust mapped at two positions on 2B. A single marker-trait was associated each to tan spot on 7B and for yellow rust on 2A. Finally, we investigated the phenotypic effect of 50 markers associated with 16 genes for resistance to rust and tan spot, and Ptr toxin reaction in a subset of 70 cultivars. We first report the marker makeup of the 70 cultivars to aid spring wheat breeders in parental choice for future crossing programs. We also identified 6-8 markers for yellow rust, 4-6 markers for leaf rust, 5-9 markers for tan spot resistance and 6-11 markers for Ptr ToxA insensitivity as the best predictors of the phenotypic variation observed across the 70 cultivars.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".