Data/hora: 16/06/2024 00:58:15

Biblioteca(s): 	Embrapa Florestas.	
Data corrente: 	02/12/2015	
Data da última atualização: 	03/01/2018	
Autoria: 	AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F.; RESENDE, M. D. V. de; LOPES, P. S.; GUIMARÃES, S. E. F.; GLÓRIA, L. S.	
Afiliação: 	C. F. AZEVEDO, Universidade Federal de Viçosa; M. NASCIMENTO, Universidade Federal de Viçosa; F. F. SILVA, Universidade Federal de Viçosa; MARCOS DEON VILELA DE RESENDE, CNPF; P. S. LOPES, Universidade Federal de Viçosa; S. E. F. GUIMARÃES, Universidade Federal de Viçosa; L. S. GLÓRIA, Universidade Federal de Viçosa.	
Título: 	Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.	
Ano de publicação: 	2015	
Fonte/Imprenta: 	Genetics and Molecular Research, Ribeirão Preto, v. 14, n. 4, p. 12217-12227, 2015.	
DOI: 	http://dx.doi.org/10.4238/2015.October.9.10	
Idioma: 	Inglês	
Conteúdo: 	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.	
Palavras-Chave: 	Componente de regressão independente; Componente principal de regressão; Componente principal parcial; Independent component regression; Partial least squares; Partial principal component; Principal component regression; Quadrados mínimos parciais.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/134673/1/2015-M.Deon-GMR-Comparison.pdf	
Registro original: 	Embrapa Florestas (CNPF)	
Exemplares: 	CNPF - (54571-1) - UPC - AP - DD -  -  -  - DIS