This data was used in the manuscript 'Genomic selection for genotype performance and stability using information on multiple traits and multiple environments' by J. Bancic, B. Ovenden, G. Gorjanc and D.J. Tolhurst.

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 using all 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, which includes a factor analytic matrix between traits, a factor analytic matrix between environments and a genomic relationship matrix between genotypes. An additional specific variance matrix is then added 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 results show that the SFA-LMM and all other factor analytic linear mixed models provide a better fit than the completely separable approaches. Selection from the SFA-LMM is then demonstrated using a selection index based on measures of genotype performance and stability. This research 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 environments.

These datasets include the phenotypic data, marker data and genomic relationship matrix used in the analysis. Phenotypic data includes grain yield, days to flowering, mature plant height and grain protein. Marker data was derived from Diversity Arrays next generation sequencing (DArTSeq).

This research was conducted as collaboration between The Roslin Institute (University of Edinburgh) and the Australian Rice Breeding Program. The Australian Rice Breeding Program is funded under the Australian Rice Partnership II project, a partnership between NSW Department of Primary Industries, AgriFutures and SunRice.