Determination of optimal number of independent components in yield traits in rice.

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dc.contributorJAQUICELE 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.
dc.creatorCOSTA, J. A. da
dc.creatorAZEVEDO, C. F.
dc.creatorNASCIMENTO, M.
dc.creatorSILVA, F. F.
dc.creatorRESENDE, M. D. V. de
dc.creatorNASCIMENTO, A. C. C.
dc.date2022-01-19T18:00:33Z
dc.date2022-01-19T18:00:33Z
dc.date2022-01-19
dc.date2022
dc.date.accessioned2026-07-01T00:27:02Z
dc.descriptionThe 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.
dc.identifierScientia Agricola, v. 79, n. 6, p. 1-8, 2022.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1139185
dc.identifierhttps://doi.org/10.1590/1678-992X-2020-0397
dc.identifier.urihttp://hdl.handle.net/123456789/404334
dc.languageeng
dc.rightsopenAccess
dc.subjectMelhoramento Genético Vegetal
dc.subjectProdutividade
dc.subjectArroz
dc.subjectGenomics
dc.subjectPlant breeding
dc.subjectYields
dc.subjectRice
dc.titleDetermination of optimal number of independent components in yield traits in rice.
dc.typeArtigo de periódico

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