Genomic Selection Accuracy for Grain Quality Traits in Biparental Wheat Populations

Genomic selection (GS) is a promising tool for plant and animal breeding that uses genome-wide molecular marker data to capture small and large effect quantitative trait loci and predict the genetic value of selection candidates. Genomic selection has been shown previously to have higher prediction accuracies than conventional marker-assisted selection (MAS) for quantitative traits. In this study, we compared phenotypic and marker-based prediction accuracy of genetic value for nine different grain quality traits within two biparental soft winter wheat (Triticum aestivum L.) populations. We used a cross-validation approach that trained and validated prediction accuracy across years to evaluate effects of model training population size, training population replication, and marker density. Results showed that prediction accuracy was significantly greater using GS versus MAS for all traits studied and that accuracy for GS reached a plateau at low marker densities (128-256). The average ratio of GS accuracy to phenotypic selection accuracy was 0.66, 0.54, and 0.42 for training population sizes of 96, 48, and 24, respectively. These results provide further empirical evidence that GS could produce greater genetic gain per unit time and cost than both phenotypic selection and conventional MAS in plant breeding with use of year-round nurseries and inexpensive, high-throughput genotyping technology.