Genome-Wide Association Studies Combined with Genomic Selection as a Tool to Increase Fusarium Head Blight Resistance in Wheat and its Wild Relatives
Bibliographic record
Abstract
Fusarium head blight (FHB) is a devastating wheat (Triticum aestivum L.) disease worldwide. Presently, there is insufficient FHB resistance in the Canadian wheat germplasm. Genome-wide association study (GWAS) and genomic selection (GS) can be utilized to identify sources of resistance that could benefit wheat breeding. To define the genetic architecture of FHB resistance, association panels from a spring and a winter collection were evaluated using the Wheat Illumina Infinium 90K array. A total of 206 accessions from the spring panel and 73 from the winter panel were evaluated in field trials for 3-4 years at two locations, namely Morden (Manitoba) and Ottawa (Ontario). These accessions were phenotyped for FHB incidence (INC), severity (SEV), visual rating index (VRI), and deoxynivalenol (DON) content. Significant (p < 0.05) differences among genotypes for all traits were found. Genetic characterization using the wheat 90K array identified a set of 20,501 single nucleotide polymorphisms (SNPs). The probe sequences (~100 bp) of these SNPs were mapped to the Chinese Spring reference genome v2.0 to identify 13,760 SNPs in the spring panel, and 10,421 SNPs in the winter panel covering all 21 wheat chromosomes. GWAS was performed to identify novel FHB resistance loci for INC, SEV, VRI and DON content for the spring and the combined panels separately using these 13,760 SNPs and for the winter panel using 10,421 SNPs. A total of 107, 157, 174 unique quantitative trait loci (QTNs) were identified for the four traits using two single-locus and seven multi-locus GWAS models for the spring, winter, and combined panels, respectively. These QTNs represent a valuable genetic resource for the improvement of FHB resistance in commercially grown wheat cultivars. In addition, these GWAS-defined QTNs were further used for GS to determine the breeding value (BV) of individuals as outlined below. In order to understand the role of the model and that of the marker type and density in trait prediction modelling, a GS study was conducted. GS is considered as an important tool for increasing genetic gain for economically important traits such as FHB resistance. GS uses genome-wide molecular markers to develop statistical models that predict genomic estimated breeding values (GEBVs) of an individual. Our results support genomic prediction (GP) as an alternative to phenotypic selection to predict the BVs of individuals for this trait. GS accounts for minor effect QTNs, which is beneficial when breeding for quantitative traits. Moderate to high GP accuracies can be achieved for FHB resistance-related traits when implemented in a breeding program. The correlation between the estimate of the missing phenotypic value and the observed phenotype is known as predictive ability (r). Overall, the predictive ability increased significantly using a QTN-based GP approach for FHB traits in wheat and its wild relatives. DON content had the highest predictive ability among all FHB traits, and that was in the winter panel, highlighting the importance of objectively measured traits in breeding for disease resistant genotypes. Interestingly, the winter panel contained several wild relative species that may harbor genes of interest to prevent the accumulation of mycotoxins in the grain. This study showed the usability of genomic prediction by improving the predictive ability of the FHB traits, which can be applied in early generation selection to accelerate the improvement of FHB resistance in wheat. The results show that GS can be successfully implemented in wheat breeding programs over multiple breeding cycles and can be effective for economically important traits. It is anticipated that GS will play a substantial role in the future of wheat breeding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".