Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.

Detalhes bibliográficos
Autor(a) principal: AZEVEDO, C. F.
Data de Publicação: 2015
Outros Autores: NASCIMENTO, M., SILVA, F. F., RESENDE, M. D. V. de, LOPES, P. S., GUIMARÃES, S. E. F., GLÓRIA, L. S.
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/1030362
http://dx.doi.org/10.4238/2015.October.9.10
Resumo: A significant contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. With this approach, genome-wide selection (GWS) can be used for this purpose. GWS consists of analyzing a large number of single nucleotide polymorphism markers widely distributed in the genome; however, because the number of markers is much larger than the number of genotyped individuals, and such markers are highly correlated, special statistical methods are widely required. Among these methods, independent component regression, principal component regression, partial least squares, and partial principal components stand out. Thus, the aim of this study was to propose an application of the methods of dimensionality reduction to GWS of carcass traits in an F2 (Piau x commercial line) pig population. The results show similarities between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.
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spelling Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.Quadrados mínimos parciaisComponente de regressão independenteComponente principal de regressãoComponente principal parcialPartial least squaresIndependent component regressionPrincipal component regressionPartial principal componentA significant contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. With this approach, genome-wide selection (GWS) can be used for this purpose. GWS consists of analyzing a large number of single nucleotide polymorphism markers widely distributed in the genome; however, because the number of markers is much larger than the number of genotyped individuals, and such markers are highly correlated, special statistical methods are widely required. Among these methods, independent component regression, principal component regression, partial least squares, and partial principal components stand out. Thus, the aim of this study was to propose an application of the methods of dimensionality reduction to GWS of carcass traits in an F2 (Piau x commercial line) pig population. The results show similarities between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.C. F. AZEVEDO, Universidade Federal de Viçosa; M. NASCIMENTO, Universidade Federal de Viçosa; F. F. SILVA, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; P. S. LOPES, Universidade Federal de Viçosa; S. E. F. GUIMARÃES, Universidade Federal de Viçosa; L. S. GLÓRIA, Universidade Federal de Viçosa.AZEVEDO, C. F.NASCIMENTO, M.SILVA, F. F.RESENDE, M. D. V. deLOPES, P. S.GUIMARÃES, S. E. F.GLÓRIA, L. S.2018-01-03T23:17:59Z2018-01-03T23:17:59Z2015-12-0220152018-01-03T23:17:59Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleGenetics and Molecular Research, Ribeirão Preto, v. 14, n. 4, p. 12217-12227, 2015.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1030362http://dx.doi.org/10.4238/2015.October.9.10enginfo: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:EMBRAPA2018-01-03T23:18:06Zoai:www.alice.cnptia.embrapa.br:doc/1030362Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542018-01-03T23:18:06Repositório 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 Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.
title Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.
spellingShingle Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.
AZEVEDO, C. F.
Quadrados mínimos parciais
Componente de regressão independente
Componente principal de regressão
Componente principal parcial
Partial least squares
Independent component regression
Principal component regression
Partial principal component
title_short Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.
title_full Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.
title_fullStr Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.
title_full_unstemmed Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.
title_sort Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.
author AZEVEDO, C. F.
author_facet AZEVEDO, C. F.
NASCIMENTO, M.
SILVA, F. F.
RESENDE, M. D. V. de
LOPES, P. S.
GUIMARÃES, S. E. F.
GLÓRIA, L. S.
author_role author
author2 NASCIMENTO, M.
SILVA, F. F.
RESENDE, M. D. V. de
LOPES, P. S.
GUIMARÃES, S. E. F.
GLÓRIA, L. S.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv C. F. AZEVEDO, Universidade Federal de Viçosa; M. NASCIMENTO, Universidade Federal de Viçosa; F. F. SILVA, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; P. S. LOPES, Universidade Federal de Viçosa; S. E. F. GUIMARÃES, Universidade Federal de Viçosa; L. S. GLÓRIA, Universidade Federal de Viçosa.
dc.contributor.author.fl_str_mv AZEVEDO, C. F.
NASCIMENTO, M.
SILVA, F. F.
RESENDE, M. D. V. de
LOPES, P. S.
GUIMARÃES, S. E. F.
GLÓRIA, L. S.
dc.subject.por.fl_str_mv Quadrados mínimos parciais
Componente de regressão independente
Componente principal de regressão
Componente principal parcial
Partial least squares
Independent component regression
Principal component regression
Partial principal component
topic Quadrados mínimos parciais
Componente de regressão independente
Componente principal de regressão
Componente principal parcial
Partial least squares
Independent component regression
Principal component regression
Partial principal component
description A significant contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. With this approach, genome-wide selection (GWS) can be used for this purpose. GWS consists of analyzing a large number of single nucleotide polymorphism markers widely distributed in the genome; however, because the number of markers is much larger than the number of genotyped individuals, and such markers are highly correlated, special statistical methods are widely required. Among these methods, independent component regression, principal component regression, partial least squares, and partial principal components stand out. Thus, the aim of this study was to propose an application of the methods of dimensionality reduction to GWS of carcass traits in an F2 (Piau x commercial line) pig population. The results show similarities between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.
publishDate 2015
dc.date.none.fl_str_mv 2015-12-02
2015
2018-01-03T23:17:59Z
2018-01-03T23:17:59Z
2018-01-03T23:17:59Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Genetics and Molecular Research, Ribeirão Preto, v. 14, n. 4, p. 12217-12227, 2015.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1030362
http://dx.doi.org/10.4238/2015.October.9.10
identifier_str_mv Genetics and Molecular Research, Ribeirão Preto, v. 14, n. 4, p. 12217-12227, 2015.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1030362
http://dx.doi.org/10.4238/2015.October.9.10
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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