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Record W2800066413 · doi:10.7939/r3fj29r36

Mapping of genomic regions associated with agronomic traits and resistance to diseases in Canadian spring wheat

2017· article· en· W2800066413 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUniversity of Alberta Library · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsSpring (device)BiologyAgronomyResistance (ecology)Plant disease resistanceDrought resistanceBiotechnologyGeneticsGeneEngineering

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.157
Teacher spread0.146 · 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