| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Florestas. |
|
Data corrente: |
21/06/2016 |
|
Data da última atualização: |
21/06/2016 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
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. |
|
Afiliação: |
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. |
|
Título: |
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs. |
|
Ano de publicação: |
2016 |
|
Fonte/Imprenta: |
Genetics and Molecular Research, v. 15, n. 2, 2016. 10 p. |
|
DOI: |
http://dx.doi.org/10.4238/gmr.15028231 |
|
Idioma: |
Inglês |
|
Conteúdo: |
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. |
|
Palavras-Chave: |
Análise multivariada; Genome enabled prediction; SNP effects. |
|
Thesagro: |
Estatística; Melhoramento genético animal; Seleção genética. |
|
Thesaurus Nal: |
Animal breeding; Multivariate analysis. |
|
Categoria do assunto: |
G Melhoramento Genético |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/144605/1/2016-M.Deon-GMR-FactorAnalysis.pdf
|
|
Marc: |
LEADER 02251naa a2200361 a 4500
001 2047516
005 2016-06-21
008 2016 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.4238/gmr.15028231$2DOI
100 1 $aTEIXEIRA, F. R. F.
245 $aFactor analysis applied to genome prediction for high-dimensional phenotypes in pigs.$h[electronic resource]
260 $c2016
520 $aThe 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.
650 $aAnimal breeding
650 $aMultivariate analysis
650 $aEstatística
650 $aMelhoramento genético animal
650 $aSeleção genética
653 $aAnálise multivariada
653 $aGenome enabled prediction
653 $aSNP effects
700 1 $aNASCIMENTO, M.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aSILVA, F. F. e
700 1 $aCRUZ, C. D.
700 1 $aAZEVEDO, C. F.
700 1 $aPAIXÃO, D. M.
700 1 $aBARROSO, L. M. A.
700 1 $aVERARDO, L. L.
700 1 $aRESENDE, M. D. V. de
700 1 $aGUIMARÃES, S. E. F.
700 1 $aLOPES, P. S.
773 $tGenetics and Molecular Research$gv. 15, n. 2, 2016. 10 p.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Florestas (CNPF) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Registros recuperados : 3 | |
| 2. |  | TEIXEIRA, F. R. F.; NASCIMENTO, M.; CECON, P. R.; CRUZ, C. D.; SILVA, F. F. e; NASCIMENTO, A. C. C.; AZEVEDO, C. F.; MARQUES, D. B. D.; SILVA, M. V. G. B.; CARNEIRO, A. P. S.; PAIXAO, D. M. Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models. Genetics and Molecular Research, v. 20, n. 1, gmr18691, 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Gado de Leite. |
|    |
| 3. | | 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. Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs. Genetics and Molecular Research, v. 15, n. 2, 2016. 10 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Florestas. |
| |
| Registros recuperados : 3 | |
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
