Determination of optimal number of independent components in yield traits in rice
ABSTRACT: 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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Escola Superior de Agricultura "Luiz de Queiroz"
2022
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oai:scielo:S0103-901620220006005012021-10-29Determination of optimal number of independent components in yield traits in riceda Costa,Jaquicele AparecidaAzevedo,Camila FerreiraNascimento,MoysésSilva,Fabyano Fonseca ede Resende,Marcos Deon VilelaNascimento,Ana Carolina Campana Oryza sativa L. genomic prediction plant breeding principal component regression independent component regression ABSTRACT: 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.info:eu-repo/semantics/openAccessEscola Superior de Agricultura "Luiz de Queiroz"Scientia Agricola v.79 n.6 20222022-01-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000600501en10.1590/1678-992x-2020-0397 |
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da Costa,Jaquicele Aparecida Azevedo,Camila Ferreira Nascimento,Moysés Silva,Fabyano Fonseca e de Resende,Marcos Deon Vilela Nascimento,Ana Carolina Campana |
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da Costa,Jaquicele Aparecida Azevedo,Camila Ferreira Nascimento,Moysés Silva,Fabyano Fonseca e de Resende,Marcos Deon Vilela Nascimento,Ana Carolina Campana Determination of optimal number of independent components in yield traits in rice |
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da Costa,Jaquicele Aparecida Azevedo,Camila Ferreira Nascimento,Moysés Silva,Fabyano Fonseca e de Resende,Marcos Deon Vilela Nascimento,Ana Carolina Campana |
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da Costa,Jaquicele Aparecida |
title |
Determination of optimal number of independent components in yield traits in rice |
title_short |
Determination of optimal number of independent components in yield traits in rice |
title_full |
Determination of optimal number of independent components in yield traits in rice |
title_fullStr |
Determination of optimal number of independent components in yield traits in rice |
title_full_unstemmed |
Determination of optimal number of independent components in yield traits in rice |
title_sort |
determination of optimal number of independent components in yield traits in rice |
description |
ABSTRACT: 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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Escola Superior de Agricultura "Luiz de Queiroz" |
publishDate |
2022 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000600501 |
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AT dacostajaquiceleaparecida determinationofoptimalnumberofindependentcomponentsinyieldtraitsinrice AT azevedocamilaferreira determinationofoptimalnumberofindependentcomponentsinyieldtraitsinrice AT nascimentomoyses determinationofoptimalnumberofindependentcomponentsinyieldtraitsinrice AT silvafabyanofonsecae determinationofoptimalnumberofindependentcomponentsinyieldtraitsinrice AT deresendemarcosdeonvilela determinationofoptimalnumberofindependentcomponentsinyieldtraitsinrice AT nascimentoanacarolinacampana determinationofoptimalnumberofindependentcomponentsinyieldtraitsinrice |
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