Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1047516
Title: Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs.
Authors: 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.
Affiliation: 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.
Date Issued: 2016
Citation: Genetics and Molecular Research, v. 15, n. 2, 2016. 10 p.
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.
Thesagro: Melhoramento genético animal
Estatística
Seleção genética
NAL Thesaurus: Animal breeding
Multivariate analysis
Keywords: Genome enabled prediction
SNP effects
Análise multivariada
DOI: http://dx.doi.org/10.4238/gmr.15028231
Type of Material: Artigo de periódico
Access: openAccess
Appears in Collections:Artigo em periódico indexado (CNPF)

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