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Registro Completo |
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Biblioteca(s): |
Embrapa Café; Embrapa Florestas. |
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Data corrente: |
06/01/2021 |
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Data da última atualização: |
27/01/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
DEL CONTE, M. V.; CARNEIRO, P. C. S.; RESENDE, M. D. V. de; SILVA, F. L. da; PETERNELLI, L. A. |
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Afiliação: |
MURILO VIOTTO DEL CONTE, UFV; PEDRO CRESCÊNCIO SOUZA CARNEIRO, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa; FELIPE LOPES DA SILVA, UFV; LUIZ ALEXANDRE PETERNELLI, UFV. |
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Título: |
Overcoming collinearity in path analysis of soybean [Glycine max (L.) Merr.] grain oil content. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
PLoS ONE, v. 15, n. 5, e0233290, 2020. 15 p. |
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DOI: |
https://doi.org/10.1371/journal.pone.0233290 |
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Idioma: |
Inglês |
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Conteúdo: |
Path analysis allows understanding the direct and indirect effects among traits. Multicollinearity in correlation matrices may cause a bias in path analysis estimates. This study
aimed to: a) understand the correlation among soybean traits and estimate their direct and indirect effects on gain oil content; b) verify the efficiency of ridge path analysis and trait culling to overcome colinearity. Three different matrices with different levels of collinearity were obtained by trait culling. Ridge path analysis was performed on matrices with strong collinearity; otherwise, a traditional path analysis was performed. The same analyses were run on a simulated dataset. Trait culling was applied to matrix R originating the matrices R1 and R2. Path analysis for matrices R1 and R2 presented a high determination coefficient (0.856 and 0.832, respectively) and low effect of the residual variable (0.379 and 0.410 respectively). Ridge path analysis presented low determination coefficient (0.657) and no direct effects greater than the effects of the residual variable (0.585). Trait culling was more effective to overcome collinearity. Mass of grains, number of nodes, and number of pods are promising for indirect selection for oil content. |
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Palavras-Chave: |
Coefficient; Components; Maturity; Multicollinearity; Seed protein; Software; Yield. |
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Thesagro: |
Melhoramento Genético Vegetal; Soja. |
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Thesaurus Nal: |
Plant breeding. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/219910/1/Marcos-Deon-pone.0233290.pdf
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Marc: |
LEADER 02124naa a2200301 a 4500
001 2129613
005 2021-01-27
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1371/journal.pone.0233290$2DOI
100 1 $aDEL CONTE, M. V.
245 $aOvercoming collinearity in path analysis of soybean [Glycine max (L.) Merr.] grain oil content.$h[electronic resource]
260 $c2020
520 $aPath analysis allows understanding the direct and indirect effects among traits. Multicollinearity in correlation matrices may cause a bias in path analysis estimates. This study aimed to: a) understand the correlation among soybean traits and estimate their direct and indirect effects on gain oil content; b) verify the efficiency of ridge path analysis and trait culling to overcome colinearity. Three different matrices with different levels of collinearity were obtained by trait culling. Ridge path analysis was performed on matrices with strong collinearity; otherwise, a traditional path analysis was performed. The same analyses were run on a simulated dataset. Trait culling was applied to matrix R originating the matrices R1 and R2. Path analysis for matrices R1 and R2 presented a high determination coefficient (0.856 and 0.832, respectively) and low effect of the residual variable (0.379 and 0.410 respectively). Ridge path analysis presented low determination coefficient (0.657) and no direct effects greater than the effects of the residual variable (0.585). Trait culling was more effective to overcome collinearity. Mass of grains, number of nodes, and number of pods are promising for indirect selection for oil content.
650 $aPlant breeding
650 $aMelhoramento Genético Vegetal
650 $aSoja
653 $aCoefficient
653 $aComponents
653 $aMaturity
653 $aMulticollinearity
653 $aSeed protein
653 $aSoftware
653 $aYield
700 1 $aCARNEIRO, P. C. S.
700 1 $aRESENDE, M. D. V. de
700 1 $aSILVA, F. L. da
700 1 $aPETERNELLI, L. A.
773 $tPLoS ONE$gv. 15, n. 5, e0233290, 2020. 15 p.
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Embrapa Café (CNPCa) |
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