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| Registros recuperados : 894 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
02/12/2015 |
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Data da última atualização: |
03/01/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F.; RESENDE, M. D. V. de; LOPES, P. S.; GUIMARÃES, S. E. F.; GLÓRIA, L. S. |
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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. |
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Título: |
Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Genetics and Molecular Research, Ribeirão Preto, v. 14, n. 4, p. 12217-12227, 2015. |
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DOI: |
http://dx.doi.org/10.4238/2015.October.9.10 |
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Idioma: |
Inglês |
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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. |
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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. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/134673/1/2015-M.Deon-GMR-Comparison.pdf
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Marc: |
LEADER 02094naa a2200301 a 4500
001 2030362
005 2018-01-03
008 2015 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.4238/2015.October.9.10$2DOI
100 1 $aAZEVEDO, C. F.
245 $aComparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.$h[electronic resource]
260 $c2015
520 $aA 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.
653 $aComponente de regressão independente
653 $aComponente principal de regressão
653 $aComponente principal parcial
653 $aIndependent component regression
653 $aPartial least squares
653 $aPartial principal component
653 $aPrincipal component regression
653 $aQuadrados mínimos parciais
700 1 $aNASCIMENTO, M.
700 1 $aSILVA, F. F.
700 1 $aRESENDE, M. D. V. de
700 1 $aLOPES, P. S.
700 1 $aGUIMARÃES, S. E. F.
700 1 $aGLÓRIA, L. S.
773 $tGenetics and Molecular Research, Ribeirão Preto$gv. 14, n. 4, p. 12217-12227, 2015.
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Embrapa Florestas (CNPF) |
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