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

• Published in: Journal of experimental botany 2023-08, Vol.74 (15), p.4415-4426
• Main Authors: Jighly, Abdulqader, Weeks, Anna, Christy, Brendan, O’Leary, Garry J, Kant, Surya, Aggarwal, Rajat, Hessel, David, Forrest, Kerrie L, Technow, Frank, Tibbits, Josquin F G, Totir, Radu, Spangenberg, German C, Hayden, Matthew J, Munkvold, Jesse, Daetwyler, Hans D
• Format: Article
• Language: English
• ISSN: 0022-0957; 1460-2431
• DOI: 10.1093/jxb/erad162

Abstract 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 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 and maturity date wheat phenology model within a CGM–WGP framework and compared it with CGM and WGP. The CGM–WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters 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 with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters 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 genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with 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. Integrating crop growth models and genomic prediction offers more accurate simulation of genotype-specific parameters with minimal equifinality even when using phenotypic data from a single field trial.