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.
Background and objectives Sponge cake quality is an essential end‐use trait for U.S. Pacific Northwest (PNW) soft white (SW) wheat. This study examined sponge cake volume of SW winter wheat germplasm from 48 balanced datasets spanning 18 years. Findings Half of the datasets returned significant whole model and variety ANOVA F‐values, compared to 35 with significant whole model and environment F‐values, indicating that environment was in general the greater source of variation. Low (mean 3.1%) coefficients of variation suggested that nonsignificant variety F‐values were due to no substantive genetic variation (as opposed to high error variance). Datasets with only two environments rarely returned significant whole model F‐values, and never significant genotype F‐values, whereas 93% of datasets with ≥5 environments had significant ANOVA models. Nevertheless, significant models did not always delineate genotypes. Infrequently were significant differences detected among the commercial varieties. Conclusions In terms of sponge cake volume, PNW soft white winter wheat varieties, especially club wheats varieties, are genetically similar and consistent. ANOVA indicated that varieties need to be grown in ≥5 environments to detect small genotype differences. Significance and novelty Sponge cake quality has remained remarkably consistent among commercial soft white winter wheat varieties over 18 years of breeding and selection. The results indicate this consistency may be due to effective testing and selection, a narrow genetic germplasm base, or a combination thereof.
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