*2.4. Statistical Analysis*

Statistical analyses were performed under the R-statistics platform (software version 4.0.2) [34]. Analyses of variance (ANOVA) for each trait were assessed by using the R package 'augmented RCBD' [35]. Phenotypic and genetic variance (PV and GV), along with genotypic and phenotypic coefficients of variation (GCV and PCV), were calculated using the formula provided by [36]. Broad sense heritability (hBS), genetic advance (GA) calculation as formula elucidated in [37]. Components of the phenotypic variance of each trait were estimated using restricted maximum likelihood methods. For the estimation of variance components of linear mixed-effect "lmer", lme4 package was employed. R package ggplot2, scales and GGally were used for heatmap analysis.

The hierarchical clustering was performed using Spearman's rank correlation algorithm. Principal component analysis (PCA) was performed using R package ggplot2, ggfortify, usethis, devtools, plyr, scales and grid. Using a two-way matrix of 10 characteristics and 130 genotypes, a G × T biplot was constructed. The first two PCs were plotted. Genotypes were schemed according to scores on each PC, and traits were plotted based on the eigenvectors on each PC. The genotypic, phenotypic variance and broad-sense heritability were estimated using Agricola R-package [38] with "metan" package. Mathematic figures were plotted using the ggplot2 package [39].
