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

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 p...

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Autores principales: Iwata, Hiroyoshi, Hayashi, Takeshi, Terakami, Shingo, Takada, Norio, Saito, Toshihiro, Yamamoto, Toshiya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847345/
https://www.ncbi.nlm.nih.gov/pubmed/24028660
http://dx.doi.org/10.1186/1471-2156-14-81
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author Iwata, Hiroyoshi
Hayashi, Takeshi
Terakami, Shingo
Takada, Norio
Saito, Toshihiro
Yamamoto, Toshiya
author_facet Iwata, Hiroyoshi
Hayashi, Takeshi
Terakami, Shingo
Takada, Norio
Saito, Toshihiro
Yamamoto, Toshiya
author_sort Iwata, Hiroyoshi
collection PubMed
description 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.
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spelling pubmed-38473452013-12-07 Genomic prediction of trait segregation in a progeny population: a case study of Japanese pear (Pyrus pyrifolia) Iwata, Hiroyoshi Hayashi, Takeshi Terakami, Shingo Takada, Norio Saito, Toshihiro Yamamoto, Toshiya BMC Genet Methodology Article 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. BioMed Central 2013-09-12 /pmc/articles/PMC3847345/ /pubmed/24028660 http://dx.doi.org/10.1186/1471-2156-14-81 Text en Copyright © 2013 Iwata et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Iwata, Hiroyoshi
Hayashi, Takeshi
Terakami, Shingo
Takada, Norio
Saito, Toshihiro
Yamamoto, Toshiya
Genomic prediction of trait segregation in a progeny population: a case study of Japanese pear (Pyrus pyrifolia)
title Genomic prediction of trait segregation in a progeny population: a case study of Japanese pear (Pyrus pyrifolia)
title_full Genomic prediction of trait segregation in a progeny population: a case study of Japanese pear (Pyrus pyrifolia)
title_fullStr Genomic prediction of trait segregation in a progeny population: a case study of Japanese pear (Pyrus pyrifolia)
title_full_unstemmed Genomic prediction of trait segregation in a progeny population: a case study of Japanese pear (Pyrus pyrifolia)
title_short Genomic prediction of trait segregation in a progeny population: a case study of Japanese pear (Pyrus pyrifolia)
title_sort genomic prediction of trait segregation in a progeny population: a case study of japanese pear (pyrus pyrifolia)
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847345/
https://www.ncbi.nlm.nih.gov/pubmed/24028660
http://dx.doi.org/10.1186/1471-2156-14-81
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