Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs.

With the current advances in the development of low-cost high-density array-based DNA marker technologies, cereal breeding programs are increasingly relying on genomic selection as a tool to accelerate the rate of genetic gain in seed quality traits. Different sources of genetic information are being explored, with the most prevalent being combined additive information from marker and pedigree-based data, and their interaction with the environment. In this, there has been mixed evidence on the performance of use of these data. This study undertook an extensive analysis of 907 elite winter barley ( L.) lines across multiple environments from two breeding companies. Six genomic prediction models were evaluated to demonstrate the effect of using pedigree and marker information individually and in combination, as well their interactions with the environment. Each model was evaluated using three cross-validation schemes that allows the prediction of newly developed lines (lines that have not been evaluated in any environment), prediction of new or unobserved years, and prediction of newly developed lines in unobserved years. The results showed that the best prediction model depends on the cross-validation scheme employed. In predicting newly developed lines in known environments, marker information had no advantage to pedigree information. Predictions in this scenario also benefited from including genotype-by-environment interaction in the models. However, when predicting lines and years not observed previously, marker information was superior to pedigree data. Nonetheless, such scenarios did not benefit from the addition of genotype-by-environment interaction. A combination of pedigree-based and marker-based information produced a similar or only marginal improvement in prediction ability. It was also discovered that combining populations from the different breeding programs to increase training population size had no advantage in prediction.