Genomic Selection in Multi-environment Crop Trials

Genomic selection in crop breeding introduces modelling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture non-additive effects. Here we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms and also randomisation-based design terms. It considers marker and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and non-marker residual genetic variation (i.e. additive and non-additive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from 2 year's trials of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analysing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment MET model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to single year standard models run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers.

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