Use of crop growth models with whole-genome prediction: application to a maize multienvironment trial

High throughput genotyping, phenotyping, and envirotyping applied within plant breeding multienvironment trials (METs) provide the data foundations for selection and tackling genotype x environment interactions (GEIs) through whole-genome prediction (WGP). Crop growth models (CGM) can be used to enable predictions for yield and other traits for different genotypes and environments within a MET if genetic variation for the influential traits and their responses to environmental variation can be incorporated into the CGM framework. Furthermore, such CGMs can be integrated with WGP to enable wholegenome prediction with crop growth models (CGM-WGP) through use of computational methods such as approximate Bayesian computation. We previously used simulated data sets to demonstrate proof of concept for application of the CGM-WGP methodology to plant breeding METs. Here the CGM-WGP methodology is applied to an empirical maize (Zea mays L.) drought MET data set to evaluate the steps involved in reduction to practice. Positive prediction accuracy was achieved for hybrid grain yield in two drought environments for a sample of doubled haploids (DHs) from a cross. This was achieved by including genetic variation for five component traits into the CGM to enable the CGM-WGP methodology. The five component traits were a priori considered to be important for yield variation among the maize hybrids in the two target drought environments included in the MET. Here, we discuss lessons learned while applying the CGM-WGP methodology to the empirical data set. We also identify areas for further research to improve prediction accuracy and to advance the CGM-WGP for a broader range of situations relevant to plant breeding