Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs.
Autor(a) principal: | |
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Data de Publicação: | 2016 |
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/1047516 http://dx.doi.org/10.4238/gmr.15028231 |
Resumo: | The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genome-wide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: ?weight?, ?fat?, ?loin?, and ?performance?. These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor. |
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spelling |
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs.Genome enabled predictionSNP effectsAnálise multivariadaMelhoramento genético animalEstatísticaSeleção genéticaAnimal breedingMultivariate analysisThe aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genome-wide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: ?weight?, ?fat?, ?loin?, and ?performance?. These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor.F. R. F. Teixeira, UFV; M. Nascimento, UFV; A. C. C. Nascimento, UFV; F. F. e Silva, UFV; C. D. Cruz, UFV; C. F. Azevedo, UFV; D. M. Paixão, UFV; L. M. A. Barroso, UFV; L. L. Verardo, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; S. E. F. Guimarães, UFV; P. S. Lopes, UFV.TEIXEIRA, F. R. F.NASCIMENTO, M.NASCIMENTO, A. C. C.SILVA, F. F. eCRUZ, C. D.AZEVEDO, C. F.PAIXÃO, D. M.BARROSO, L. M. A.VERARDO, L. L.RESENDE, M. D. V. deGUIMARÃES, S. E. F.LOPES, P. S.2016-06-22T11:21:30Z2016-06-22T11:21:30Z2016-06-2120162016-06-22T11:21:30Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleGenetics and Molecular Research, v. 15, n. 2, 2016. 10 p.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1047516http://dx.doi.org/10.4238/gmr.15028231enginfo: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:EMBRAPA2017-08-16T03:38:12Zoai:www.alice.cnptia.embrapa.br:doc/1047516Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542017-08-16T03:38:12falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542017-08-16T03:38:12Repositório 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 |
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs. |
title |
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs. |
spellingShingle |
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs. TEIXEIRA, F. R. F. Genome enabled prediction SNP effects Análise multivariada Melhoramento genético animal Estatística Seleção genética Animal breeding Multivariate analysis |
title_short |
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs. |
title_full |
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs. |
title_fullStr |
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs. |
title_full_unstemmed |
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs. |
title_sort |
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs. |
author |
TEIXEIRA, F. R. F. |
author_facet |
TEIXEIRA, F. R. F. NASCIMENTO, M. NASCIMENTO, A. C. C. SILVA, F. F. e CRUZ, C. D. AZEVEDO, C. F. PAIXÃO, D. M. BARROSO, L. M. A. VERARDO, L. L. RESENDE, M. D. V. de GUIMARÃES, S. E. F. LOPES, P. S. |
author_role |
author |
author2 |
NASCIMENTO, M. NASCIMENTO, A. C. C. SILVA, F. F. e CRUZ, C. D. AZEVEDO, C. F. PAIXÃO, D. M. BARROSO, L. M. A. VERARDO, L. L. RESENDE, M. D. V. de GUIMARÃES, S. E. F. LOPES, P. S. |
author2_role |
author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
F. R. F. Teixeira, UFV; M. Nascimento, UFV; A. C. C. Nascimento, UFV; F. F. e Silva, UFV; C. D. Cruz, UFV; C. F. Azevedo, UFV; D. M. Paixão, UFV; L. M. A. Barroso, UFV; L. L. Verardo, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; S. E. F. Guimarães, UFV; P. S. Lopes, UFV. |
dc.contributor.author.fl_str_mv |
TEIXEIRA, F. R. F. NASCIMENTO, M. NASCIMENTO, A. C. C. SILVA, F. F. e CRUZ, C. D. AZEVEDO, C. F. PAIXÃO, D. M. BARROSO, L. M. A. VERARDO, L. L. RESENDE, M. D. V. de GUIMARÃES, S. E. F. LOPES, P. S. |
dc.subject.por.fl_str_mv |
Genome enabled prediction SNP effects Análise multivariada Melhoramento genético animal Estatística Seleção genética Animal breeding Multivariate analysis |
topic |
Genome enabled prediction SNP effects Análise multivariada Melhoramento genético animal Estatística Seleção genética Animal breeding Multivariate analysis |
description |
The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genome-wide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: ?weight?, ?fat?, ?loin?, and ?performance?. These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-06-22T11:21:30Z 2016-06-22T11:21:30Z 2016-06-21 2016 2016-06-22T11:21:30Z |
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. 15, n. 2, 2016. 10 p. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1047516 http://dx.doi.org/10.4238/gmr.15028231 |
identifier_str_mv |
Genetics and Molecular Research, v. 15, n. 2, 2016. 10 p. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1047516 http://dx.doi.org/10.4238/gmr.15028231 |
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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1794503422858231808 |