A framework for building enviromics matrices in mixed models

Abstract

This study unravels a framework for constructing enviromics matrices within mixed models to integrate genetic and envirotypic data, enhancing phenotypic predictions in plant breeding. Enviromics leverages diverse data sources, such as climate and soil, to characterize genotype-by-environment (G×E) interactions. The approach uses block-diagonal structures in the design matrix to incorporate random effects from genetic and envirotypic covariates across trials. The covariance structure is modeled through the Kronecker product of the genetic relationship matrix and an identity matrix representing envirotypic effects, effectively capturing both genetic and environmental variability. This dual representation facilitates more accurate predictions of crop performance across environments, enabling improved selection strategies in breeding programs. The framework is compatible with widely used mixed model software, including rrBLUP and BGLR, and is adaptable to account for more complex interactions. By integrating genetic relationships and environmental influences, this approach provides a robust tool for advancing G×E studies and accelerating the development of superior crop varieties.