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Record W3110467072 · doi:10.7939/r3-tfg8-7m81

Mapping QTLs for different traits in conventional and organic management systems and evaluating the effects of Lr34/Yr18 and Lr37/Yr17 in a Canadian western hard spring wheat population

2019· article· en· W3110467072 on OpenAlexaboutno aff
Rongrong Xiang

Bibliographic record

VenueUniversity of Alberta Library · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationSpring (device)Environmental scienceAgronomyGeographyBiologyEngineeringDemographySociology

Abstract

fetched live from OpenAlex

Canadian western red spring wheat (CWRS) has been predominantly cultivated class in Western Canada, because of its premium quality attributes and excellent adaptability to the relatively short growing season. Early maturity, short plant stature, higher grain yield, protein content and dough strength, and moderate to high levels of resistance to stem rust, leaf rust, stripe rust, common bunt, and fusarium head blight are important breeding objectives in western Canada. In the first study, we evaluated a mapping population of 168 recombinant inbred lines derived from a cross between two CWRS cultivars ‘Peace’ and ‘CDC Stanley’ for agronomic and quality traits under organic and conventional managements from 2016 to 2017. Days to heading and maturity, grain yield and protein content, thousand kernel weight (TKW) and test weight expressed high broad-sense heritability across two management systems. The population was genotyped with 90K single nucleotide polymorphism (SNP) array and quantitative trait loci (QTL) analysis was performed. However, only six of 50 QTLs could be detected across two management systems. The phenotypic variance explained for each trait varied from 0.5 – 23.3 % in conventional and 1.3 – 25.9 % in organic environment. A QTL on chromosome 2D was associated with multiple traits (plant height, grain yield, grain protein content and test weight in conventional and days to maturity, grain yield and plant height in organic environment), which is possibly due to tight linkage of multiple loci on this chromosomal segment, whereas another coincidental QTL on 4B for grain yield and protein content in conventional management system could be due to pleiotropic effect. We also validated a major pre-harvest sprouting (PHS) resistance QTL Qphs-usask-4A in the mapping population. PHS resistant genotypes possessed significantly higher falling number, however standardized methods are required to examine the effect of Qphs-usask-4A.The second study was to investigate the combined effects of Lr34/Yr18 and Lr37/Yr17 genes on disease resistance in the same mapping population. Lines with only Lr34/Yr18 expressed reduced plant height, SDS sedimentation and yield penalty, possibly due to genetic linkage or pleiotropic effects of co-expression of leaf tip necrosis on flag leaf and rust resistance. The presence of Lr37/Yr17 was not associated with reduction in grain yield or end-use quality. In the breeding practice, we failed to integrate Lr34/Yr18 and Lr37/Yr17 genes with considerable grain yield, protein content, dough strength and early maturity, which was most likely due to insufficient population size. Thus, whole-scale dependence on markers in a marker-assisted selection program will likely eliminate desirable genotypes. Nonetheless, five lines with substantial disease resistance conferred by the Lr34/Yr18 and/or Lr37/Yr17 resistance alleles and improved agronomic and quality characters remain in the breeding process that has potential to become parental materials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.189
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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