Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.

Bibliographic Details
Main Author: SOUSA, T. V.
Publication Date: 2019
Other Authors: CAIXETA, E. T., ALKIMIM, E. R., OLIVEIRA, A. C. B. de, PEREIRA, A. A., SAKIYAMA, N. S., ZAMBOLIM, L., RESENDE, M. D. V. de
Format: Article
Language: eng
Source: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Download full: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1106798
Summary: Genomic Selection (GS) has allowed the maximization of genetic gains per unit time in several annual and perennial plant species. However, no GS studies have addressed Coffea arabica, the most economically important species of the genus Coffea. Therefore, this study aimed (i) to evaluate the applicability and accuracy of GS in the prediction of the genomic estimated breeding value (GEBV); (ii) to estimate the genetic parameters; and (iii) to evaluate the time reduction of the selection cycle by GS in Arabica coffee breeding. A total of 195 Arabica coffee individuals, belonging to 13 families in generation of F2, susceptible backcross and resistant backcross, were phenotyped for 18 agronomic traits, and genotyped with 21,211 SNP molecular markers. Phenotypic data, measured in 2014, 2015, and 2016, were analyzed by mixed models. GS analyses were performed by the G-BLUPmethod, using the RKHS (Reproducing Kernel Hilbert Spaces) procedure, with a Bayesian algorithm. Heritabilities and selective accuracies were estimated, revealing moderate to high magnitude for most of the traits evaluated. Results of GS analyses showed the possibility of reducing the cycle time by 50%, maximizing selection gains per unit time. The effect of marker density on GS analyses was evaluated. Genomic selection proved to be promising for C. arabica breeding. The agronomic traits presented high complexity for they are controlled by several QTL and showed low genomic heritabilities, evidencing the need to incorporate genomic selection methodologies to the breeding programs of this species.
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spelling Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.Genetic gainsSelective efficiencyGenomic-enabled prediction accuracySNP molecular markerPlant breedingGenomic Selection (GS) has allowed the maximization of genetic gains per unit time in several annual and perennial plant species. However, no GS studies have addressed Coffea arabica, the most economically important species of the genus Coffea. Therefore, this study aimed (i) to evaluate the applicability and accuracy of GS in the prediction of the genomic estimated breeding value (GEBV); (ii) to estimate the genetic parameters; and (iii) to evaluate the time reduction of the selection cycle by GS in Arabica coffee breeding. A total of 195 Arabica coffee individuals, belonging to 13 families in generation of F2, susceptible backcross and resistant backcross, were phenotyped for 18 agronomic traits, and genotyped with 21,211 SNP molecular markers. Phenotypic data, measured in 2014, 2015, and 2016, were analyzed by mixed models. GS analyses were performed by the G-BLUPmethod, using the RKHS (Reproducing Kernel Hilbert Spaces) procedure, with a Bayesian algorithm. Heritabilities and selective accuracies were estimated, revealing moderate to high magnitude for most of the traits evaluated. Results of GS analyses showed the possibility of reducing the cycle time by 50%, maximizing selection gains per unit time. The effect of marker density on GS analyses was evaluated. Genomic selection proved to be promising for C. arabica breeding. The agronomic traits presented high complexity for they are controlled by several QTL and showed low genomic heritabilities, evidencing the need to incorporate genomic selection methodologies to the breeding programs of this species.TIAGO VIEIRA SOUSA, BIOAGRO/BioCafé/Universidade Federal de Viçosa - UFVEVELINE TEIXEIRA CAIXETA, CNPCaEMILLY RUAS ALKIMIM, Universidade Federal do Triângulo Mineiro - UFTMANTONIO CARLOS BAIAO DE OLIVEIRA, CNPCaANTONIO ALVES PEREIRA, Empresa de Pesquisa Agropecuária de Minas Gerais – EpamigNEY SUSSUMU SAKIYAMA, Universidade Federal de Viçosa - UFV/Departamento de FitotecniaLAÉRCIO ZAMBOLIM, Universidade Federal de Viçosa - UFV/Departamento de FitopatologiaMARCOS DEON VILELA DE RESENDE, CNPF.SOUSA, T. V.CAIXETA, E. T.ALKIMIM, E. R.OLIVEIRA, A. C. B. dePEREIRA, A. A.SAKIYAMA, N. S.ZAMBOLIM, L.RESENDE, M. D. V. de2019-03-09T00:34:09Z2019-03-09T00:34:09Z2019-03-0820192019-10-17T11:11:11Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleFrontiers in Plant Science, v. 9, January 2019.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1106798enginfo: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:EMBRAPA2019-03-09T00:34:16Zoai:www.alice.cnptia.embrapa.br:doc/1106798Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542019-03-09T00:34:16falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542019-03-09T00:34:16Repositório 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 Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.
title Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.
spellingShingle Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.
SOUSA, T. V.
Genetic gains
Selective efficiency
Genomic-enabled prediction accuracy
SNP molecular marker
Plant breeding
title_short Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.
title_full Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.
title_fullStr Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.
title_full_unstemmed Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.
title_sort Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding.
author SOUSA, T. V.
author_facet SOUSA, T. V.
CAIXETA, E. T.
ALKIMIM, E. R.
OLIVEIRA, A. C. B. de
PEREIRA, A. A.
SAKIYAMA, N. S.
ZAMBOLIM, L.
RESENDE, M. D. V. de
author_role author
author2 CAIXETA, E. T.
ALKIMIM, E. R.
OLIVEIRA, A. C. B. de
PEREIRA, A. A.
SAKIYAMA, N. S.
ZAMBOLIM, L.
RESENDE, M. D. V. de
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv TIAGO VIEIRA SOUSA, BIOAGRO/BioCafé/Universidade Federal de Viçosa - UFV
EVELINE TEIXEIRA CAIXETA, CNPCa
EMILLY RUAS ALKIMIM, Universidade Federal do Triângulo Mineiro - UFTM
ANTONIO CARLOS BAIAO DE OLIVEIRA, CNPCa
ANTONIO ALVES PEREIRA, Empresa de Pesquisa Agropecuária de Minas Gerais – Epamig
NEY SUSSUMU SAKIYAMA, Universidade Federal de Viçosa - UFV/Departamento de Fitotecnia
LAÉRCIO ZAMBOLIM, Universidade Federal de Viçosa - UFV/Departamento de Fitopatologia
MARCOS DEON VILELA DE RESENDE, CNPF.
dc.contributor.author.fl_str_mv SOUSA, T. V.
CAIXETA, E. T.
ALKIMIM, E. R.
OLIVEIRA, A. C. B. de
PEREIRA, A. A.
SAKIYAMA, N. S.
ZAMBOLIM, L.
RESENDE, M. D. V. de
dc.subject.por.fl_str_mv Genetic gains
Selective efficiency
Genomic-enabled prediction accuracy
SNP molecular marker
Plant breeding
topic Genetic gains
Selective efficiency
Genomic-enabled prediction accuracy
SNP molecular marker
Plant breeding
description Genomic Selection (GS) has allowed the maximization of genetic gains per unit time in several annual and perennial plant species. However, no GS studies have addressed Coffea arabica, the most economically important species of the genus Coffea. Therefore, this study aimed (i) to evaluate the applicability and accuracy of GS in the prediction of the genomic estimated breeding value (GEBV); (ii) to estimate the genetic parameters; and (iii) to evaluate the time reduction of the selection cycle by GS in Arabica coffee breeding. A total of 195 Arabica coffee individuals, belonging to 13 families in generation of F2, susceptible backcross and resistant backcross, were phenotyped for 18 agronomic traits, and genotyped with 21,211 SNP molecular markers. Phenotypic data, measured in 2014, 2015, and 2016, were analyzed by mixed models. GS analyses were performed by the G-BLUPmethod, using the RKHS (Reproducing Kernel Hilbert Spaces) procedure, with a Bayesian algorithm. Heritabilities and selective accuracies were estimated, revealing moderate to high magnitude for most of the traits evaluated. Results of GS analyses showed the possibility of reducing the cycle time by 50%, maximizing selection gains per unit time. The effect of marker density on GS analyses was evaluated. Genomic selection proved to be promising for C. arabica breeding. The agronomic traits presented high complexity for they are controlled by several QTL and showed low genomic heritabilities, evidencing the need to incorporate genomic selection methodologies to the breeding programs of this species.
publishDate 2019
dc.date.none.fl_str_mv 2019-03-09T00:34:09Z
2019-03-09T00:34:09Z
2019-03-08
2019
2019-10-17T11:11:11Z
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 Frontiers in Plant Science, v. 9, January 2019.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1106798
identifier_str_mv Frontiers in Plant Science, v. 9, January 2019.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1106798
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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