(2013) compared the genetic gain in grain yield and stover-quality traits between GS and conventional marker-assisted recurrent selection, and showed that the genetic gains were larger with GS. In plant species, several applied studies have used GS in maize and wheat. GS has already been implemented and shown robust success in animal breeding ( Hayes et al., 2009). In simulations, GS provided superior efficiency in terms of genetic gain per year and total cost per genetic gain by saving time, cost and the effort required for phenotypic observations ( Bernardo and Yu, 2007 Heffner et al., 2010, 2011). In GS, a training population, which has been phenotyped and genotyped, is used to construct a model that predicts genetic potential (genomic estimated breeding value, GEBV) of unphenotyped individuals by using genome-wide genotype data ( Meuwissen et al., 2001).
Genomic selection (GS) is now widely accepted as an efficient method for improving genetically complex traits ( Desta and Ortiz, 2014). Although further analyses are required to apply GS in an actual breeding situation, our results indicated that GS is a promising strategy for future tomato breeding design. The GS models also predicted phenotypes of progeny, although their efficiency varied depending on the parental cross combinations and the selected traits. The GS models successfully predicted a better parent even if the phenotypic value did not vary substantially between candidates. The present study includes two experiments: a prediction of a parental combination that generates superior progeny and the prediction of progeny phenotypes. A collection of big-fruited F 1 varieties was used to construct the GS models, and the progeny from crosses was used to validate the models. We assessed the potential of GS for increasing soluble solids content and total fruit weight of tomato. Genomic selection (GS), which uses estimated genetic potential based on genome-wide genotype data for a breeding selection, is now widely accepted as an efficient method to improve genetically complex traits.