Genomic Prediction of Trait Segregation in Progeny Populations: Examples in Japanese Pear and Rice

In cross breeding, it is important to choose a “good” parental combination that has high probability of generating offspring with desired characteristics. In the present study, we proposed a method for predicting the segregation pattern of target traits in a progeny population based on the genome-wide marker and phenotype data of parental varieties. In the proposed method, we combined segregation simulation and Bayesian modeling for genomic selection. Marker segregation in a progeny population was simulated based on parental genotypes. Posterior of marker effects sampled via Markov Chain Monte Carlo (MCMC) were used to predict the segregation pattern of target traits. The posterior probability of obtaining progeny lines/individuals that meet selection criteria and can use the probability to determine the promising crosses and the required size of a progeny population. Japanese pear (Pyrus phyrifolia) and rice (Oryza sativa) data were used for illustrating the proposed method and demonstrating how it works in the selection of promising crosses. Empirical studies using real segregating populations in Japanese pear and rice suggested that the segregation pattern could be predicted with reasonable accuracy especially in highly heritable traits, such as harvest time and heading date. In lower heritable traits such as fruit weight, the uncertainty of the prediction was reflected on the posterior distribution of probability of obtaining acceptable progeny. The proposed method is useful to provide objective and quantitative criteria for choosing a parental combination and the size of a breeding population.