Mapping of quantitative trait loci (QTLs) in Thinopyrum intermedium for Fusarium graminearum tolerance and agronomic traits
Bibliographic record
Abstract
Despite the significant benefits that intermediate wheatgrass (Thinopyrum intermedium) provides for soil health and carbon sequestration, its potential in Canada's commercial production landscape is yet to be fully realized. As a result, the University of Manitoba's perennial grains breeding program is focused on developing IWG cultivars with increased grain yields and greater resistance to Fusarium graminearum. Genetic analysis was performed to improve our understanding of these traits in IWG. A genetic map consisting of 865 markers covering 20 linkage groups was developed in R using the package OneMap (Margarido et al., 2007). The map was developed using genetic and phenotypic data from 150 F1 progeny and two parents (UM2014-0001 and UM2014-0044) from cycle two UMB selections. The JGI public access intermediate wheatgrass consensus map (v3.1) was used as a reference. Quantitative trait locus (QTL) analysis was performed using composite interval mapping (CIM) in the R package fullsibQTL (Gazaffi et al., 2014) with a LOD threshold of 3. The results revealed 52 QTLs associated with Fusarium head blight (FHB) severity and incidence measurements and 52 QTLs associated with various agronomic traits. One large effect QTL for seed number and two large effect QTLs associated with FHB severity and incidence were identified. The future use of marker assisted selection in the UMB population is discussed in relation to 13 relevant QTLs. Broad-sense heritability estimates revealed plant height, thousand kernel weight, and seed number were highly heritable traits, while tiller number and crown diameter were largely controlled by environmental factors. Among this population, reciprocal crosses involving UM2014-0001 exhibited significantly larger seed masses, while UM2014-0002 parents produced 22.8% more seeds. UM2014-0044 parents displayed superiority in forage related traits (crown diameter and plant height). Trait relationships and genetic analysis of agronomic and FHB resistance traits has provided insights to advance future IWG breeding programs. This research enables marker-assisted selection to breed for IWG crops that meet the demands of our food system while addressing the environmental challenges this system has created.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".