Unraveling complex traits in wheat: Approaches for analyzing genotype × environment interactions in a multienvironment study of falling numbers

Abstract

AbstractMultienvironment trials provide useful information about highly variable, complex plant traits like yield and quality but are difficult to analyze due to their frequently unbalanced nature, with genotypes and locations varying from year to year. Our objective was to use multiple approaches, including joint regression and principal components analysis, to characterize patterns in the genotype × environment interactions across an unbalanced 3‐yr multienvironment wheat (Triticum aestivum L.) variety trial dataset, examining falling number (FN) test results in wheat. The FN test measures the decrease in flour gelling capacity resulting from starch digestion by the enzyme α‐amylase. Low FN and high‐α‐amylase grain is discounted because it is associated with poor end‐use quality. Low FN can be caused by susceptibility either to preharvest sprouting when it rains before harvest or to late‐maturity α‐amylase induction by temperature fluctuations during grain maturation. The most effective and visually intuitive approach for selecting varieties with high FN across variable environments was a combination of joint regression, such as Finlay–Wilkinson and Eberhart and Russell, with biplot methods such as the additive main effects and multiplicative interaction model (AMMI) and the genotype main effects and genotype × environment interaction model (GGE). We identify stable lines for FN resistance and provide a means to analyze unbalanced, multienvironment data from breeding and variety trials.