Analysis of Genotype × Environment Interaction (G×E) Using SAS Programming

Abstract

Genotype × environment interaction (G×E) can lead to differences in the performance of genotypes across environments. A G×E analysis can be used to analyze the stability of genotypes and the value of test locations. We developed a SAS program (SASG×E) that calculates univariate stability statistics, descriptive statistics, pooled and yearly ANOVA, genotypic and location variation, cluster analysis for location, and correlations among stability parameters. Univariate stability statistics calculated are Wricke's ecovalence ( W i 2 ), Shukla's variance (σ i 2 ), Lin and Binns cultivar superiority measure ( P i ), Francis and Kannenberg coefficient of variation (CV i ), Kang's yield stability statistic (YS i ), Perkins and Jinks β (β i ), regression slope ( b i ), and deviation from regression ( S d 2 ). Other output includes input files for analyzing stability in R software using AMMI and GGEBiplotGUI packages. SASG×E uses SAS programming language features (macro and structured query language [SQL]) for repetitive tasks, making it efficient and flexible for the simultaneous analysis of multiple dependent variables. SASG×E is free and intended for use by scientists studying the performance of polygenic or quantitative traits in multiple environments. The SASG×E program is presented here and is also available at http://cuke.hort.ncsu.edu/cucurbit/wehner/software.html . Core Ideas Genotype × environment interaction can lead to differences in genotype performance. G×E analysis can analyze genotype stability and the value of test locations. SASG×E uses SAS and R programming to compute uni‐ and multi‐variate stability statistics. SASG×E output includes univariate stability statistics, ready to go input files, and R code for AMMI and GGE biplot analysis, ANOVA, descriptive statistics, cluster analysis of location, rank correlation among stability parameters, and Pearson correlation of location with average location performance.