Genomic prediction of sugar content and cane yield in sugar cane clones in different stages of selection in a breeding program, with and without pedigree information

Abstract

High cane yield and commercially extractable sucrose (CCS) content are two of the key sugarcane commercial traits selected in sugarcane breeding programs. Advancements in genomic prediction may provide opportunities to speed up gains for these traits in breeding programs by combining accurate prediction of breeding values in candidate parent clones shortening generation intervals. Selection trials in commercial breeding programs may provide training populations for developing genomic predictions. In this study, three different populations of clones in early and advanced stage selection trials in an established commercial sugarcane breeding program were used to assess genomic prediction accuracy. The clones (genotypes) were evaluated for cane yield and sugar content in field trials and genotyped using a SNP array developed for sugarcane cultivars and parents. Five models (Bayes A, Bayes B, Bayesian LASSO, Bayesian GBLUP and RKHS) were tested using pedigree and/or marker data. Prediction models that included marker information had higher prediction accuracies than models with pedigree data only. For CCS, the prediction accuracies for genotypes in advanced stage trials using DNA markers were superior compared with prediction accuracies for early-stage trials, suggesting that prior intensive selection for CCS did not diminish accuracy of genomic prediction. However, by contrast, for cane yield, the prediction accuracies were much less for the population in the advanced stages of selection. The levels of prediction accuracy obtained in most datasets (0.25–0.45) are encouraging for developing applications of genomic prediction to predict breeding values of yield and sugar content in sugarcane breeding programs.