CBGG

Genetically correlated traits can be used for improving predictive abilities of genomic predictions including several traits in multi-trait models. Here, the wheat quality traits thousand-kernel weight, grain protein content, Zeleny sedimentation, and falling number were phenotyped in 1152 advanced winter wheat lines from four cycles of a commercial breeding program. Multi-trait and trait-assisted genomic prediction models including two or four traits were studied and compared with single-trait models. In the trait-assisted genomic predictions, breeding values of the trait of interest were predicted for lines that had been phenotyped for additional traits. Predictive abilities of single-trait models ranged from 0.5 for thousand-kernel weight to 0.65 for falling number based on 10-fold cross-validations. Predictive abilities were in most cases not significantly different between single- and multi-trait models, when no phenotypic data was included for lines in the validation set. However, predictive abilities for grain protein content increased when using trait-assisted models, where the phenotypic data for Zeleny sedimentation or falling number were available for the lines in the validation set. The trait-assisted models also resulted in increased predictive abilities for Zeleny sedimentation, when phenotypic data for grain protein content was included. The latter situation could be relevant for breeding programs for improving wheat quality.

plant breeding; wheat quality; genomic prediction; multi-trait; Zeleny sedimentation; grain protein content; thousand-kernel weight; falling number