Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: A case study in wheat phenology.

Running crop growth models (CGM) coupled with whole genome prediction (WGP), as a CGM-WGP model, introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters (GSPs) modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading date and a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it to CGM and WGP. The CGM-WGP resulted in more heritable GSPs with more biologically realistic correlation structures between GSPs and phenology traits compared to CGM-modelled GSPs that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared to that required for CGM. A genome-wide association analysis linked the GSPs from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled GSPs. Selection on GSPs could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared to the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.