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Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel

KEY MESSAGE: Rice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement. ABSTRACT: So far, most potential applications of genomic prediction in plant impro...

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Autores principales: Ben Hassen, M., Cao, T. V., Bartholomé, J., Orasen, G., Colombi, C., Rakotomalala, J., Razafinimpiasa, L., Bertone, C., Biselli, C., Volante, A., Desiderio, F., Jacquin, L., Valè, G., Ahmadi, N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5787227/
https://www.ncbi.nlm.nih.gov/pubmed/29138904
http://dx.doi.org/10.1007/s00122-017-3011-4
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author Ben Hassen, M.
Cao, T. V.
Bartholomé, J.
Orasen, G.
Colombi, C.
Rakotomalala, J.
Razafinimpiasa, L.
Bertone, C.
Biselli, C.
Volante, A.
Desiderio, F.
Jacquin, L.
Valè, G.
Ahmadi, N.
author_facet Ben Hassen, M.
Cao, T. V.
Bartholomé, J.
Orasen, G.
Colombi, C.
Rakotomalala, J.
Razafinimpiasa, L.
Bertone, C.
Biselli, C.
Volante, A.
Desiderio, F.
Jacquin, L.
Valè, G.
Ahmadi, N.
author_sort Ben Hassen, M.
collection PubMed
description KEY MESSAGE: Rice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement. ABSTRACT: So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F(5)–F(7) lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r (2) = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41–0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00122-017-3011-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-57872272018-02-02 Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel Ben Hassen, M. Cao, T. V. Bartholomé, J. Orasen, G. Colombi, C. Rakotomalala, J. Razafinimpiasa, L. Bertone, C. Biselli, C. Volante, A. Desiderio, F. Jacquin, L. Valè, G. Ahmadi, N. Theor Appl Genet Original Article KEY MESSAGE: Rice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement. ABSTRACT: So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F(5)–F(7) lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r (2) = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41–0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00122-017-3011-4) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2017-11-14 2018 /pmc/articles/PMC5787227/ /pubmed/29138904 http://dx.doi.org/10.1007/s00122-017-3011-4 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Ben Hassen, M.
Cao, T. V.
Bartholomé, J.
Orasen, G.
Colombi, C.
Rakotomalala, J.
Razafinimpiasa, L.
Bertone, C.
Biselli, C.
Volante, A.
Desiderio, F.
Jacquin, L.
Valè, G.
Ahmadi, N.
Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel
title Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel
title_full Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel
title_fullStr Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel
title_full_unstemmed Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel
title_short Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel
title_sort rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5787227/
https://www.ncbi.nlm.nih.gov/pubmed/29138904
http://dx.doi.org/10.1007/s00122-017-3011-4
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