Publications by authors named "A L Brule-Babel"
Background: Fusarium head blight (FHB), caused by Fusarium graminearum, is a major disease of wheat in North America. FHB infection causes fusarium damaged kernels (FDKs), accumulation of deoxynivalenol (DON) in the grain, and a reduction in quality and grain yield. Inheritance of FHB resistance is complex and involves multiple genes.
View Article and Find Full Text PDFFusarium head blight (FHB) is one the most globally destructive fungal diseases in wheat and other small grains, causing a reduction in grain yield by 10-70%. The present study was conducted in a panel of historical and modern Canadian spring wheat ( L.) varieties and lines to identify new sources of FHB resistance and map associated quantitative trait loci (QTLs).
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- This study compared the predictive abilities of single-trait (ST) and multi-trait (MT) models in three spring wheat populations, revealing that multi-trait models consistently outperformed single-trait models across various agronomic and disease resistance traits.
- Analysis showed that the multi-trait model evaluated for some traits but not others (MT2) achieved the highest average prediction accuracy (0.67), outperforming both the single-trait model (0.41) and the first multi-trait model (MT1, 0.47).
- The multi-traits models, particularly MT2, significantly increased prediction accuracies across all traits, with improvements ranging from 9.0% to 82.4%
Article Synopsis
- This study investigates genomic selection (GS) in wheat, focusing on the effects of reaction norm models on predicting disease resistance to stripe rust, leaf rust, Fusarium head blight, and leaf spot, while also addressing previously unexplored prediction accuracy for common bunt.
- Three Canadian spring wheat populations were tested in various field environments and genotyped with a substantial number of polymorphic markers to analyze prediction accuracies using different model frameworks.
- The results show that the M3 model, which accounts for genotype-environment (GE) interactions, significantly improves prediction accuracy by reducing residual variance and achieving high predictions (up to 87%) for several diseases compared to the basic M2 model.
Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat.
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