Reducing And Exploiting Genotype By Environment Interaction In The Context Of Genomewide Prediction In 969 Maize Biparental Populations

Abstract

Multi-environment testing remains crucial in genomewide selection, and environmental effects (Ej) complicate selection. We aimed to: 1) determine if past year’s data on previous populations can be used to eliminate environments for a current training population; 2) assess if genomewide predictions can reduce the number of environments used in subsequent phenotypic selection; 3) identify which statistical models and environmental factors are best for estimating Ej; and 4) determine the predictive ability in models that include and exclude genotype × environment interaction effects. A total of 969 Monsanto maize (Zea mays L.) populations were genotyped and phenotyped at multiple U.S. locations from 2000 to 2008. Environmental data from the National Oceanic and Atmospheric Administration were gathered and interpolated. The data included 154,000 lines, 448 million marker data points, 3.2 million phenotypic observations, 1395 unique environments, and 1.3 million environmental covariable data points. For 27 biparental crosses that we chose as test populations, environmental stability and an index that used genomewide predictions and phenotypic data could replace one out of four environments in phenotypic evaluation. Correlations between predicted and observed Ej were between 0.25 and 0.35 even when only two environmental factors (precipitation and heat units) were used. A nonfactorial model for line performance in a given environment effectively combined both the line genetic effect and Ej, doubling prediction ability for grain yield and test weight. We speculate that this model can be combined with crop modelling for additional prediction ability in predicting plant performance in a given environment.