Title:	Overcoming collinearity in path analysis of soybean [Glycine max (L.) Merr.] grain oil content.
Authors:	DEL CONTE, M. V. CARNEIRO, P. C. S. RESENDE, M. D. V. de SILVA, F. L. da PETERNELLI, L. A.
Affiliation:	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.
Date Issued:	2020
Citation:	PLoS ONE, v. 15, n. 5, e0233290, 2020. 15 p.
Description:	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.
Thesagro:	Soja Melhoramento Genético Vegetal
NAL Thesaurus:	Plant breeding
Keywords:	Seed protein Yield Multicollinearity Coefficient Components Software Maturity
DOI:	https://doi.org/10.1371/journal.pone.0233290
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPF)