Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems

Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction

“…In the present study, the stripe rust prediction accuracies in the CV1 were significantly smaller than both the CV2 and CV0 regardless of the models and populations, which were negative or nearly zero when only phenotypes were used in the M1 model and low to moderate (0.19-0.49) when both phenotypes and molecular markers were used with and without incorporating GEI (Tables 2 and S2B). Such results restrict the breeder's ability to confidently implement GS to develop stripe-rust-resistant spring wheat varieties, which agree with several previous studies that reported similar accuracies in multiple traits [22,[32][33][34][35][36].…”

“…The RILs were advanced to F 6 using the single seed descent method, while the DH lines were developed from F 1 s at the Agriculture and Agri-Food Canada (AAFC) Research and Development Center in Lethbridge, AB, CA, using the wheat-maize hybridization method [59]. The diversity panel, the Peace/Carberry, and the Attila/CDC Go populations were previously used to compare prediction accuracies of 7 agronomic and end-use quality traits [36] and resistance to wheat diseases [22]. The methodologies for disease phenotyping, DNA preparations, genotyping, and genotype data filtering were described in our previous study [22].…”

Genome‐wide association mapping of agronomic traits and grain characteristics in spring wheat under conventional and organic management systems

“…Recently, we assessed the suitability of single-trait genomic prediction accuracies on seven agronomic and end-use quality traits recorded in three spring wheat populations under conventional and organic management systems. Our results revealed no statistically significant differences in prediction accuracies between the two management systems (Semagn et al, 2022), which provide breeders an opportunity to use phenotype data generated in one management for predicting the performance of lines in another management. Overall, the average prediction accuracies of the model that mimicked predicting new (future) environments (CV0) and reducing the number of phenotyping environments (CV2) varied from 0.69 to 0.97.…”

“…Overall, the average prediction accuracies of the model that mimicked predicting new (future) environments (CV0) and reducing the number of phenotyping environments (CV2) varied from 0.69 to 0.97. The prediction accuracies of the CV1 scheme that mimicked predicting the performance of lines that have not been tested at any environment ranged from −0.12 to 0.77 depending on the models, the traits, and genetic backgrounds (Semagn et al., 2022). Similar results were obtained using multi‐trait prediction models (Semagn et al., unpublished).…”

“…The two breeding approaches for simultaneously improving these negatively correlated traits involve either (a) selecting genotypes with early maturity and high GPC with acceptable grain yield and plant height and/or (b) selecting short stature and high yielding genotypes with acceptable maturity and GPC. Both methods may be successful if crosses are selected based on multi‐trait linear phenotypic selection indices and/or multi‐trait genomic selection using genome‐wide markers instead of QTL‐specific markers (Lehermeier et al., 2017; Semagn et al., 2022), which are addressed in separate studies.…”