Environment characterization and genomic prediction for end‐use quality traits in soft white winter wheat. Issue 3 (15th August 2021) - Aoun, Meriem; Carter, Arron; Thompson, Yvonne A.; Ward, Brian; Morris, Craig F.
Abstract: End‐use quality phenotyping is laborious and expensive, thus, testing may not occur until later generations in wheat breeding programs. We investigated the pattern of genotype × environment (G × E) interaction for end‐use quality traits in soft white wheat ( Triticum aestivum L.) and tested the effectiveness of implementing genomic selection to optimize breeding for these traits. We used a multi‐environment unbalanced dataset comprised of 672 breeding lines and cultivars adapted to the Pacific Northwest region of the United States, which were evaluated for 14 end‐use quality traits. Genetic correlations between environments based on factor analytic models showed low‐to‐moderate G × E interaction for most traits but high G × E interaction for grain and flour protein. A total of 40, 518 single‐nucleotide polymorphism markers were used for genomic prediction. Genomic prediction accuracies were high for most traits thereby justifying the use of genomic selection to assist breeding for superior end‐use quality in soft white wheat. Excluding outlier environments based on genetic correlations between environments was more effective in increasing genomic prediction accuracies compared with that based on environment clustering analysis. For kernel size, kernel weight, milling score, ash, and flour swelling volume, excluding outlier environments increased prediction accuracies by 1–11%. However, for grain and flour protein, flour yield, and cookie diameter, excluding outlier environments did not improve genomic prediction performance. Core Ideas: End‐use quality traits are laborious and expensive to phenotype. Factor analytic models were successful in understanding genotype × environment interaction. Genomic prediction accuracies were high for most end‐use quality traits in soft white wheat. Environment clustering and genetic correlations identified outlier environments. For some traits, excluding outlier environments improved prediction performance.