Genomic Selection of Promising Parental Combinations: A Case Study in Japanese Pear

In cross breeding, it is important to choose a good parental combination that has high probability of generating offspring with desired characteristics. Genomic selection (GS) is an attractive technology to surmount fruit tree breeding problems, e.g. their long lifespan and large plant size, because it enables selection without field-testing. GS is useful not only for selecting promising individuals but also for selecting promising parental combinations. We recently proposed a method for predicting the segregation of target traits in a progeny population based on the genome-wide marker and phenotype data of parental cultivars. The proposed method combines segregation simulation and Bayesian modeling for genomic selection, which allows the proportion of progenies that fulfill selection criteria to be calculated and is used for determining a promising cross and the necessary size of the progeny population. We applied the method to Japanese pear (Pyrus pyrifolia Nakai) data consisting of genotypes of 333 markers and phenotypes of 12 agronomic traits of 84 cultivars. Because traits were recorded with ordinal categorical scores, we modeled an unrecorded continuous variable that underlies recorded ordinal scores. Verification using an actual breeding population suggests that the segregation of target traits can be predicted with reasonable accuracy, especially in a highly heritable trait. The uncertainty in predictions was reflected on the posterior distribution of the proportion of progenies that fulfill selection criteria. The proposed method is useful to provide objective and quantitative criteria for choosing a parental combination and the breeding population size.