Mapping Quantitative Trait Loci Associated with Agronomic Traits and Disease Resistance in a Canadian Spring Wheat Mapping Population
Bibliographic record
Abstract
Due to reduced genotyping costs and high-throughput technologies, marker assisted selection has become a valuable tool for plant breeders, allowing for identifying traits of interest in screened germplasm. Marker assisted selection requires the identification of stable, and consistent quantitative trait loci (QTL) that will become reliable markers. The Canada Western Red Spring (CWRS) class of common wheat (T. aestivum) is the most produced class of wheat in western Canada and requires a complex arrangement of agronomic traits and adequate disease resistance. The objective of this thesis was to identify QTL associated with economically important diseases in western Canada such as stripe rust (Puccinia striiformis f. sp. tritici), leaf rust (Puccinia triticina), and the leaf spot complex, and agronomic traits that are important to producers and end users including earliness, grain yield, protein content and gluten strength. A total of 208 recombinant inbred lines derived from crossing Canadian spring wheat (T. aestivum) cultivars ‘Peace’ and ‘Carberry’ were evaluated from 2014 to 2017 in disease nurseries located in Alberta and British Columbia, and conventional and organic yield trials in 2016 and 2017 in Edmonton, Alberta and genotyped with DArTseq markers. Using the least squares means of the combined environments, three QTL associated with stripe rust, four QTL associated with leaf rust, and three QTL associated with leaf spotting were identified. We confirm the presence of a stripe rust QTL on chromosome 4B, with the allele conferring resistance contributed by ‘Carberry’, that has been previously reported by others. We also identified two QTL associated with stripe rust and leaf rust on chromosome 2A, in which the alleles conferring resistance were contributed by ‘Peace’, corresponding with previous studies that identified QTL on chromosome 2A that were contributed by a close relative of ‘Peace’. Phenotyping of agronomic traits was conducted in conventional and organic environments to identify consistent QTL across management systems. We identified thirty-eight QTL for nine agronomic traits and QTL clusters on chromosomes 4B and 7D were identified consistently across conventional and organic environments. The largest QTL was identified as an allele contributed by ‘Carberry’, and is most likely the Rht-B1b height reducing gene, due to explaining 53% of the total phenotypic variance and being located on 4B. The second largest QTL was located on chromosome 1A and associated with sedimentation volume and explained 41% of the total phenotypic variance. Results from this study suggest that ‘Carberry’ could be an attractive germplasm for breeders to enhance resistance against stripe rust and leaf spot with minor resistance alleles, and ‘Peace’ consistently contributed an allele on 7D that reduced plant height by six centimeters, and maturity by two days, but reduced grain yield by 300 kg ha-1. Minor effect QTL with LOD scores as low as 3.4 were consistently identified across management-specific environments and suggests that stable QTL may not need to be large in effect.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".