Genomic selection for genotype performance and stability using information on multiple traits and multiple environments

This paper develops a single-stage genomic selection (GS) approach which incorporates information on multiple traits and multiple environments within a partially separable factor analytic framework. The factor analytic linear mixed model is an effective method for analysing multi-environment trial (MET) datasets, but is yet to be extended to GS for multiple traits and multiple environments. The advantage of incorporating all three sources of information is that breeders can utilise genotype by environment by trait interaction (GETI) to obtain more accurate predictions across correlated traits and environments. The partially separable factor analytic linear mixed model (SFA-LMM) developed in this paper is based on a three-way separable structure, with a factor analytic model for traits, a factor analytic model for environments and a genomic relationship matrix for genotypes. This structure is then modified to enable a different genotype by environment interaction (GEI) pattern for each trait, and a different genotype by trait interaction (GTI) pattern for each environment. The SFA-LMM is demonstrated on a multi-trait MET dataset from The Australian Rice Breeding Program. Selection within the rice breeding program is demonstrated using a selection index based on measures of genotype performance and stability. This approach represents an important continuation in the advancement of plant breeding analyses, particularly with the advent of high-throughput phenotypic datasets involving a very large number of traits and/or environments.