Genomic prediction of trait segregation in a progeny population: a case study of Japanese pear (Pyrus pyrifolia) View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-09-12

AUTHORS

Hiroyoshi Iwata, Takeshi Hayashi, Shingo Terakami, Norio Takada, Toshihiro Saito, Toshiya Yamamoto

ABSTRACT

BackgroundIn 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.ResultsThe 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.ConclusionsThe proposed method is useful to provide objective and quantitative criteria for choosing a parental combination and the breeding population size. More... »

PAGES

81

References to SciGraph publications

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    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2156-14-81

    DOI

    http://dx.doi.org/10.1186/1471-2156-14-81

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1002538615

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24028660


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