Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1086472 |
Resumo: | In genomic recurrent selection programs of self-pollinated crops, additive genetic effects (breeding values) are effectively relevant for selection of superior progenies as new parents. However, considering additive and nonadditive genetic effects can improve the prediction of genome-enhanced breeding values (GEBV) of progenies, for quantitative traits. In this study, we assessed the magnitude of additive and nonadditive genetic variances for eight key traits in a rice population under recurrent selection, using marker-based relationship matrices. We then assessed the goodness-to-fit, bias, stability and accuracy of prediction for breeding values and total (additive plus nonadditive) genetic values, in five genomic best linear unbiased prediction (GBLUP) models, ignoring or not nonadditive genetic effects. The models were compared using 6174 single nucleotide polymorphisms (SNP) markers from 174 S1:3 progenies evaluated in field yield trial. We found dominance effects accounting for a substantial proportion of the total genetic variance for the key traits in rice, especially for days to flowering. In average of the traits, the component of variance additive, dominance, and epistatic contributed to about 34%, 14% and 9% for phenotypic variance. Additive genomic models, ignoring nonadditive genetic effects, showed better fit to the data and lower bias, in addition to greater stability and accuracy for predict GEBV of progenies. These results improve our understanding of the genetic architecture of the key traits in rice, evaluated in early-generation testing. Clearly, this study highlighted the advantages of additive models using genome-wide information, for genomic prediction applied to recurrent selection in a self-pollinated crop. |
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Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breedingGenetic architecturePredictive accuracyGBLUP modelsVariance componentsArrozOryza sativaMelhoramento genético vegetalSeleção recorrenteRicePlant breedingquantitative traitsIn genomic recurrent selection programs of self-pollinated crops, additive genetic effects (breeding values) are effectively relevant for selection of superior progenies as new parents. However, considering additive and nonadditive genetic effects can improve the prediction of genome-enhanced breeding values (GEBV) of progenies, for quantitative traits. In this study, we assessed the magnitude of additive and nonadditive genetic variances for eight key traits in a rice population under recurrent selection, using marker-based relationship matrices. We then assessed the goodness-to-fit, bias, stability and accuracy of prediction for breeding values and total (additive plus nonadditive) genetic values, in five genomic best linear unbiased prediction (GBLUP) models, ignoring or not nonadditive genetic effects. The models were compared using 6174 single nucleotide polymorphisms (SNP) markers from 174 S1:3 progenies evaluated in field yield trial. We found dominance effects accounting for a substantial proportion of the total genetic variance for the key traits in rice, especially for days to flowering. In average of the traits, the component of variance additive, dominance, and epistatic contributed to about 34%, 14% and 9% for phenotypic variance. Additive genomic models, ignoring nonadditive genetic effects, showed better fit to the data and lower bias, in addition to greater stability and accuracy for predict GEBV of progenies. These results improve our understanding of the genetic architecture of the key traits in rice, evaluated in early-generation testing. Clearly, this study highlighted the advantages of additive models using genome-wide information, for genomic prediction applied to recurrent selection in a self-pollinated crop.ODILON PEIXOTO MORAIS JUNIOR; JOAO BATISTA DUARTE, UFG; FLAVIO BRESEGHELLO, CNPAF; ALEXANDRE S. G. COELHO, UFG; TEREZA CRISTINA DE OLIVEIRA BORBA, CNPAF; JORDENE T. AGUIAR; PERICLES DE CARVALHO FERREIRA NEVES, CNPAF; ORLANDO PEIXOTO DE MORAIS, CNPAF.MORAIS JÚNIOR, O. P.DUARTE, J. B.BRESEGHELLO, F.COELHO, A. S. G.BORBA, T. C. O.AGUIAR, J. T.NEVES, P. C. F.MORAIS, O. P.2018-01-26T23:48:25Z2018-01-26T23:48:25Z2018-01-2620172018-01-26T23:48:25Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleGenetics and Molecular Research, v. 16, n. 4, gmr16039849, Dec. 2017.1676-5680http://www.alice.cnptia.embrapa.br/alice/handle/doc/108647210.4238/gmr16039849enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2018-01-26T23:48:33Zoai:www.alice.cnptia.embrapa.br:doc/1086472Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542018-01-26T23:48:33falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542018-01-26T23:48:33Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding |
title |
Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding |
spellingShingle |
Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding MORAIS JÚNIOR, O. P. Genetic architecture Predictive accuracy GBLUP models Variance components Arroz Oryza sativa Melhoramento genético vegetal Seleção recorrente Rice Plant breeding quantitative traits |
title_short |
Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding |
title_full |
Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding |
title_fullStr |
Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding |
title_full_unstemmed |
Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding |
title_sort |
Relevance of additive and non-additive genetic relatedness for genomic prediction in rice population under recurrent selection breeding |
author |
MORAIS JÚNIOR, O. P. |
author_facet |
MORAIS JÚNIOR, O. P. DUARTE, J. B. BRESEGHELLO, F. COELHO, A. S. G. BORBA, T. C. O. AGUIAR, J. T. NEVES, P. C. F. MORAIS, O. P. |
author_role |
author |
author2 |
DUARTE, J. B. BRESEGHELLO, F. COELHO, A. S. G. BORBA, T. C. O. AGUIAR, J. T. NEVES, P. C. F. MORAIS, O. P. |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
ODILON PEIXOTO MORAIS JUNIOR; JOAO BATISTA DUARTE, UFG; FLAVIO BRESEGHELLO, CNPAF; ALEXANDRE S. G. COELHO, UFG; TEREZA CRISTINA DE OLIVEIRA BORBA, CNPAF; JORDENE T. AGUIAR; PERICLES DE CARVALHO FERREIRA NEVES, CNPAF; ORLANDO PEIXOTO DE MORAIS, CNPAF. |
dc.contributor.author.fl_str_mv |
MORAIS JÚNIOR, O. P. DUARTE, J. B. BRESEGHELLO, F. COELHO, A. S. G. BORBA, T. C. O. AGUIAR, J. T. NEVES, P. C. F. MORAIS, O. P. |
dc.subject.por.fl_str_mv |
Genetic architecture Predictive accuracy GBLUP models Variance components Arroz Oryza sativa Melhoramento genético vegetal Seleção recorrente Rice Plant breeding quantitative traits |
topic |
Genetic architecture Predictive accuracy GBLUP models Variance components Arroz Oryza sativa Melhoramento genético vegetal Seleção recorrente Rice Plant breeding quantitative traits |
description |
In genomic recurrent selection programs of self-pollinated crops, additive genetic effects (breeding values) are effectively relevant for selection of superior progenies as new parents. However, considering additive and nonadditive genetic effects can improve the prediction of genome-enhanced breeding values (GEBV) of progenies, for quantitative traits. In this study, we assessed the magnitude of additive and nonadditive genetic variances for eight key traits in a rice population under recurrent selection, using marker-based relationship matrices. We then assessed the goodness-to-fit, bias, stability and accuracy of prediction for breeding values and total (additive plus nonadditive) genetic values, in five genomic best linear unbiased prediction (GBLUP) models, ignoring or not nonadditive genetic effects. The models were compared using 6174 single nucleotide polymorphisms (SNP) markers from 174 S1:3 progenies evaluated in field yield trial. We found dominance effects accounting for a substantial proportion of the total genetic variance for the key traits in rice, especially for days to flowering. In average of the traits, the component of variance additive, dominance, and epistatic contributed to about 34%, 14% and 9% for phenotypic variance. Additive genomic models, ignoring nonadditive genetic effects, showed better fit to the data and lower bias, in addition to greater stability and accuracy for predict GEBV of progenies. These results improve our understanding of the genetic architecture of the key traits in rice, evaluated in early-generation testing. Clearly, this study highlighted the advantages of additive models using genome-wide information, for genomic prediction applied to recurrent selection in a self-pollinated crop. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2018-01-26T23:48:25Z 2018-01-26T23:48:25Z 2018-01-26 2018-01-26T23:48:25Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Genetics and Molecular Research, v. 16, n. 4, gmr16039849, Dec. 2017. 1676-5680 http://www.alice.cnptia.embrapa.br/alice/handle/doc/1086472 10.4238/gmr16039849 |
identifier_str_mv |
Genetics and Molecular Research, v. 16, n. 4, gmr16039849, Dec. 2017. 1676-5680 10.4238/gmr16039849 |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1086472 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503448805244928 |