A Framework for Building Enviromics Matrices in Mixed Models

TL;DR

This paper presents a new framework for integrating genetic and environmental data into mixed models to improve phenotypic predictions in plant breeding, leveraging enviromics matrices and advanced covariance structures.

Contribution

It introduces a novel method for constructing enviromics matrices with block-diagonal structures and Kronecker product covariance modeling in mixed models.

Findings

Enhanced prediction accuracy for crop performance across environments

Compatibility with existing mixed model software like rrBLUP and BGLR

Facilitates advanced GxE interaction analysis in breeding programs

Abstract

This study introduces a framework for constructing enviromics matrices in mixed models to integrate genetic and environmental data to enhance phenotypic predictions in plant breeding. Enviromics utilizes diverse data sources, such as climate and soil, to characterize genotype-by-environment (GxE) interactions. The approach employs block-diagonal structures in the design matrix to incorporate random effects from genetic and envirotypic covariates across trials. The covariance structure is modeled using the Kronecker product of the genetic relationship matrix and an identity matrix representing envirotypic effects, capturing genetic and environmental variability. This dual representation enables more accurate crop performance predictions across environments, improving selection strategies in breeding programs. The framework is compatible with existing mixed model software, including rrBLUP and BGLR, and can be extended for more complex interactions. By combining genetic relationships and environmental influences, this approach offers a powerful tool for advancing GxE studies and accelerating the development of improved crop varieties.