Optimising accuracy of performance predictions using available morphophysiological information in wheat breeding germplasm

Wheat breeding programmes often harbour large number of developed progenies. Testing of all progenies in many target environments is not considered cost‐effective, so the genomic predictions are employed. Genomic predictions might be enhanced with adaptational traits given the cost‐effectiveness of their phenotyping. The aim of this study was to assess effectiveness of adding a priori available phenotyping data of adaptational morpho‐physiological traits to genomic predictions in Bayesian framework. The panel of 120 winter wheat genotypes was sown over four growing seasons (2013/2014–2016/2017) in field conditions in two replicates and phenotyped for target traits grain yield and plant height along with nine distinctness, uniformity and stability (DUS) traits. Five genotype groups were defined by the K‐means clustering method based on nine DUS traits. DUS traits were used as fixed covariates in five different genomic prediction models with different assumptions about the distribution densities of marker effects: Bayes A, Bayes B, Bayes C, Bayesian ridge regression and Bayesian lasso. Adding DUS traits as covariates significantly improved accuracies and reduced the root mean square errors (RMSEP) of genomic predictions. Marginal differences between different models were observed. Adding covariates to genomic prediction models might be good strategy to improve accuracies of the predictions by accounting for environmental adaptations, provided their a priori availability or low costs of additional phenotyping. Models fitting the genomewide markers have been proved as efficient tool for performance predictions. These genomic prediction models can be enhanced by adding the DUS traits, available in most breeding programs as covariates into the DUS‐enhanced genomic prediction models at the same cost.