Data/hora: 21/09/2024 20:52:35

Biblioteca(s): 	Embrapa Florestas.	
Data corrente: 	21/06/2016	
Data da última atualização: 	21/06/2016	
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: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/144605/1/2016-M.Deon-GMR-FactorAnalysis.pdf	
Registro original: 	Embrapa Florestas (CNPF)	
Exemplares: 	CNPF - (55090-1) - UPC - AP - DD -  -  -  - DIS