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., Lopes, P.S., Guimarães, S.E.F., Glória, L.S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: LOCUS Repositório Institucional da UFV
Texto Completo: http://dx.doi.org/10.4238/2015.October.9.10
http://www.locus.ufv.br/handle/123456789/11977
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 pigsPartial 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.Genetics and Molecular Research2017-10-10T14:27:22Z2017-10-10T14:27:22Z2015-10-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlepdfapplication/pdf16765680http://dx.doi.org/10.4238/2015.October.9.10http://www.locus.ufv.br/handle/123456789/11977engv.14 n.(4): 12217-12227 October 2015Azevedo, C.F.Nascimento, M.Silva, F.F.Resende, M.D.V.Lopes, P.S.Guimarães, S.E.F.Glória, L.S.info:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFV2024-07-12T07:42:24Zoai:locus.ufv.br:123456789/11977Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452024-07-12T07:42:24LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)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.
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.
Lopes, P.S.
Guimarães, S.E.F.
Glória, L.S.
author_role author
author2 Nascimento, M.
Silva, F.F.
Resende, M.D.V.
Lopes, P.S.
Guimarães, S.E.F.
Glória, L.S.
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Azevedo, C.F.
Nascimento, M.
Silva, F.F.
Resende, M.D.V.
Lopes, P.S.
Guimarães, S.E.F.
Glória, L.S.
dc.subject.por.fl_str_mv Partial least squares
Independent component regression
Principal component regression
Partial principal component
topic 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 2015-10-09
2017-10-10T14:27:22Z
2017-10-10T14:27:22Z
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 16765680
http://dx.doi.org/10.4238/2015.October.9.10
http://www.locus.ufv.br/handle/123456789/11977
identifier_str_mv 16765680
url http://dx.doi.org/10.4238/2015.October.9.10
http://www.locus.ufv.br/handle/123456789/11977
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv v.14 n.(4): 12217-12227 October 2015
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv pdf
application/pdf
dc.publisher.none.fl_str_mv Genetics and Molecular Research
publisher.none.fl_str_mv Genetics and Molecular Research
dc.source.none.fl_str_mv reponame:LOCUS Repositório Institucional da UFV
instname:Universidade Federal de Viçosa (UFV)
instacron:UFV
instname_str Universidade Federal de Viçosa (UFV)
instacron_str UFV
institution UFV
reponame_str LOCUS Repositório Institucional da UFV
collection LOCUS Repositório Institucional da UFV
repository.name.fl_str_mv LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)
repository.mail.fl_str_mv fabiojreis@ufv.br
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