02124naa a2200301 a 450000100080000000500110000800800410001902400540006010000210011424501230013526000090025852012470026765000190151465000350153365000090156865300160157765300150159365300130160865300220162165300170164365300130166065300100167370000230168370000250170670000200173170000220175177300490177321296132021-01-27       2020    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1371/journal.pone.02332902DOI1 aDEL CONTE, M. V.  aOvercoming collinearity in path analysis of soybean [Glycine max (L.) Merr.] grain oil content.h[electronic resource]  c2020  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.  aPlant breeding  aMelhoramento Genético Vegetal  aSoja  aCoefficient  aComponents  aMaturity  aMulticollinearity  aSeed protein  aSoftware  aYield1 aCARNEIRO, P. C. S.1 aRESENDE, M. D. V. de1 aSILVA, F. L. da1 aPETERNELLI, L. A.  tPLoS ONEgv. 15, n. 5, e0233290, 2020. 15 p.