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Record W7029024125

Genomics-assisted assessment of quantitative disease resistance: an evaluation of genetic resistance of two-row barley (Hordeum vulgare L.) to fusarium head blight (Fusarium graminearum) and deoxynivalenol accumulation

2022· dissertation· en· W7029024125 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2022
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsnot available
Fundersnot available
KeywordsFusariumTrichotheceneQuantitative trait locusMycotoxinHordeum vulgareVomitoxinPopulationPlant breedingPlant disease resistanceGenetic marker
DOInot available

Abstract

fetched live from OpenAlex

Fusarium head blight (FHB) incited by Fusarium graminearum, is currently the most devastating disease of barley (Hordeum vulgare L.) in western Canada. Type B trichothecene mycotoxins e.g. deoxynivalenol (DON) associated with FHB are closely monitored by both malting and livestock feed industries. Breeding disease-resistant varieties is a sustainable disease management practice, however, this is complicated by quantitative-based resistance. To date, breeding efforts have largely relied on labour-intensive screening nurseries. Two studies focus on the evaluation of a single nucleotide polymorphic (SNP) marker array (Barley 50K SNP) in i) genome-wide association study (GWAS); ii) genomic estimated breeding values (GEBV) whole-genome marker prediction. While GWAS identified numerous genomic associations with FHB and DON, they generally were small effects explaining a low proportion of the phenotypic variance. Genomic estimated breeding values (GEBVs) were calculated by classical statistical and machine learning approaches, where both were able to demonstrate moderate prediction is possible for FHB and DON content. Genomic selection is thus recommended as an efficient breeding approach vs. considerations of individual SNP markers. The F. graminearum population of western Canada has shifted towards 3-acetyl-deoxynivalenol (3ADON)-producers, which tend to accumulate higher levels of DON. In vitro selection (IVS) has been successfully used to identify resistant somaclonal variants. Another study involves poly-A RNA-sequencing contrast of IVS derived variety Norman vs. its parent, CDC Kendall. Different transcriptional alternations were detected in response to infection by 3ADON, 15ADON, and nivalenol (NIV) – producing strains of F. graminearum. In the final experiment, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to evaluate mycotoxin composition in rachis and grains for a group of field-inoculated varieties with differential resistance levels. The conjugate form deoxynivalenol-3-glucoside (DON3G) was found to occur at a significant level (26%) of total toxins. While varieties differed in DON content, the ratio of DON3G/DON appeared stable across genotypes. In summary, the dissertation contributed to novel research through the identification of feasible genomics-based breeding strategies for developing FHB resistance and lowering DON, contributed to an understanding of IVS resistance of Norman, identified gene targets of differentially expressed genes between the varieties, and has identified toxin-conjugation as a resistance factor.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.304
Teacher spread0.252 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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