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| Registros recuperados : 316 | |
| Registros recuperados : 316 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
19/01/2022 |
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Data da última atualização: |
19/01/2022 |
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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: |
COSTA, J. A. da; AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F.; RESENDE, M. D. V. de; NASCIMENTO, A. C. C. |
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Afiliação: |
JAQUICELE APARECIDA DA COSTA, UFV; CAMILA FERREIRA AZEVEDO, UFV; MOYSÉS NASCIMENTO, UFV; FABYANO FONSECA E SILVA, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa; ANA CAROLINA CAMPANA NASCIMENTO, UFV. |
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Título: |
Determination of optimal number of independent components in yield traits in rice. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Scientia Agricola, v. 79, n. 6, p. 1-8, 2022. |
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DOI: |
https://doi.org/10.1590/1678-992X-2020-0397 |
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Idioma: |
Inglês |
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Conteúdo: |
The principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined that the number of ICs should be equal to the number of PCs to reach a higher accuracy value. In addition, the criteria did not recover the simulated heritability and generated biased genomic values. |
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Thesagro: |
Arroz; Melhoramento Genético Vegetal; Produtividade. |
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Thesaurus NAL: |
Genomics; Plant breeding; Rice; Yields. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/230385/1/determination-of-optimal-number.pdf
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Marc: |
LEADER 01768naa a2200277 a 4500
001 2139185
005 2022-01-19
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1590/1678-992X-2020-0397$2DOI
100 1 $aCOSTA, J. A. da
245 $aDetermination of optimal number of independent components in yield traits in rice.$h[electronic resource]
260 $c2022
520 $aThe principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined that the number of ICs should be equal to the number of PCs to reach a higher accuracy value. In addition, the criteria did not recover the simulated heritability and generated biased genomic values.
650 $aGenomics
650 $aPlant breeding
650 $aRice
650 $aYields
650 $aArroz
650 $aMelhoramento Genético Vegetal
650 $aProdutividade
700 1 $aAZEVEDO, C. F.
700 1 $aNASCIMENTO, M.
700 1 $aSILVA, F. F.
700 1 $aRESENDE, M. D. V. de
700 1 $aNASCIMENTO, A. C. C.
773 $tScientia Agricola$gv. 79, n. 6, p. 1-8, 2022.
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Embrapa Café (CNPCa) |
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