title: Genomic prediction of trait segregation in a progeny population: a case study of Japanese pear (Pyrus pyrifolia).
abstract: BACKGROUND
  In cross breeding, it is important to choose a good parental combination that has high probability of generating offspring with desired characteristics. This study examines a method for predicting the segregation of target traits in a progeny population based on genome-wide markers and phenotype data of parental cultivars.
  
  
  RESULTS
  The proposed method combines segregation simulation and Bayesian modeling for genomic selection. Marker segregation in a progeny population was simulated based on parental genotypes. Posterior marker effects sampled via Markov Chain Monte Carlo were used to predict the segregation pattern of target traits. The posterior distribution of the proportion of progenies that fulfill selection criteria was calculated and used for determining a promising cross and the necessary size of the progeny population. We applied the proposed method to Japanese pear (Pyrus pyrifolia Nakai) data to demonstrate the method and to show how it works in the selection of a promising cross. 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. A simulation study based on the real marker data of Japanese pear cultivars also suggests the potential of the method.
  
  
  CONCLUSIONS
  The proposed method is useful to provide objective and quantitative criteria for choosing a parental combination and the breeding population size.
authors.affiliation: Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, 113-8657, Tokyo, Japan. aiwata@mail.ecc.u-tokyo.ac.jp.
authors.forename: Hiroyoshi
authors.initials: H
authors.lastname: Iwata
authors.forename: Takeshi
authors.initials: T
authors.lastname: Hayashi
authors.forename: Shingo
authors.initials: S
authors.lastname: Terakami
authors.forename: Norio
authors.initials: N
authors.lastname: Takada
authors.forename: Toshihiro
authors.initials: T
authors.lastname: Saito
authors.forename: Toshiya
authors.initials: T
authors.lastname: Yamamoto
mesh_terms: D001499:Bayes Theorem; D001947:Breeding; D016678:Genome; D005838:Genotype; D006801:Humans; D007564:Japan; D008390:Markov Chains; D009010:Monte Carlo Method; D010641:Phenotype; D031989:Pyrus / Q000235:genetics*; D019655:Quantitative Trait, Heritable
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