Overcoming collinearity in path analysis of soybean [Glycine max (L.) Merr.] grain oil content

Abstract

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 R 1 and R 2. Path analysis for matrices R 1 and R 2 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.

Structural Equation Models to Interpret Multi-Trait Genome-Wide Association Studies for Morphological and Productive Traits in Soybean [Glycine max (L.) Merr.]

“…For NG, Ghiday et al 30 reported a heritability of 0.98. Similarly, Del Conte et al 31 and de Albuquerque et al 32 reported heritability estimates of 0.70 and 0.59, respectively. For HGW, Bisinotto et al 33 , reported a heritability estimate of 0.77 using 31 lines from a breeding program.…”

“…The genomic correlation estimates were consistent with those found in the literature. According to Del Conte et al 31 and Li et al 34 , the genetic correlation estimates between NP and NG were 0.87 and 0.88, respectively.…”

Development of Novel Blackgram (Vigna mungo (L.) Hepper) Mutants and Deciphering Genotype × Environment Interaction for Yield-Related Traits of Mutants

“…Assessing genetic variability present in germplasm using morphological traits by multivariate analysis and understanding the associations between seed yield and yield-related traits would facilitate the selection of progenies through breeding cycles. This was studied earlier in blackgram [16,17] and soybean [18,19].…”

Association between traits related to fruit quality in papaya (Carica papaya L.) hybrids and their implications for selection

“…Although the correlation study between variables is a great tool in plant breeding programs, the study does not allow to observe the effect that a variable provides on the other, making the correlation coefficients unfeasible in indicating variables for indirect selection as they do not take into account the causes and effects of this association (Del Conte et al., 2020). To overcome this problem, Wright (1921) developed a method called a path analysis, which allows the dissociation of the correlation coefficients in the direct effect (DE) and indirect effect (IE) of a group of independent variables on a main dependent variable.…”