A Factor Analytic Mixed Model Approach For The Analysis Of Genotype By Treatment By Environment Data

The accurate evaluation of genotype performance for a range of traits, including disease resistance, is of great importance to the productivity and sustainability of major Australian commercial crops. Typically, the data generated from crop evaluation programmes arise from a series of field trials known as multi-environment trials (METs), which investigate genotype performance over a range of environments.

In evaluation trials for disease resistance, it is not uncommon for some genotypes to be chemically treated against the afflicting disease. An important example in Australia is the assessment of genotypes for resistance to blackleg disease in canola crops where it is common practice to treat canola seeds with a fungicide. Genotypes are either grown in trials as treated, untreated or as both.

There are a number of methods for the analysis of MET data. These methods, however, do not specifically address the analysis of data with an underlying three-way structure of genotype by treatment by environment (GxTxE). Here, we propose an extension of the factor analytic mixed model approach for MET data, using the canola blackleg data as the motivating example.

Historically in the analysis of blackleg data, the factorial genotype by treatment structure of the data was not accounted for. Entries, which are the combinations of genotypes and fungicide treatments present in trials, were regarded as `genotypes’ and a two-way analysis of ‘genotypes’ by environments was conducted.

The analysis of our example showed that the accuracy of genotype predictions, and thence information for growers, was substantially improved with the use of the three-way GxTxE approach compared with the historical approach.