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.
journal_name
BMC Genetjournal_title
BMC geneticsauthors
Iwata H,Hayashi T,Terakami S,Takada N,Saito T,Yamamoto Tdoi
10.1186/1471-2156-14-81subject
Has Abstractpub_date
2013-09-12 00:00:00pages
81issn
1471-2156pii
1471-2156-14-81journal_volume
14pub_type
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