Characterization of maize populations in different environmental conditions by means of Three-Mode Principal Components Analysis

Abstract

The experimental design generated three-way or three-mode data, repeated observations of a set of attributes for a set of individuals in different conditions. The information was displayed in a three-dimensional array, and the structure of the data was explored using Three-Mode Principal Component Analysis, the Tucker-2 Model. A group of populations was identified that displayed homogeneous behavior in the two environments with respect to the following traits: ear length, prolificacy, grains per meter, yield and 1000 kernel weight. However, another group of populations displayed opposite behavior for the traits of plant height and ear insertion height in the different environment conditions and is indicative of the existence of genotype-environment interaction. In conclusion, Three-Mode Principal Component Analysis is an important tool for characterizing plant genetic resources when their phenotypic values are likely to be affected by environmental conditions.

Three-Way Multivariate Analysis for the Characterization of Plant Genetic Resources

“…There are even authors who have called it a multivariate extension of the Additive Main effects and Multiplicative Interactions (AMMI) models [15]. But its interpretation is not easy [16], and there is few software that do this, among them the R package tuckerR.mmgg [17]. The Escoufier Vectorial Correlation Coefficient (Rv) [18], which determines whether two configurations (scalar products) provide a similar image of individuals, taking values between zero (no similarity between the configurations) and one (the configurations are homothetic), can be used to quantify the degree of multivariate genotype-environment interaction [13].…”