Support Vector Machine Regression for the Prediction of Maize Hybrid Performance

Accurate prediction of the phenotypical performance of untested single-cross hybrids allows for a faster genetic progress of the breeding pool at a reduced cost. We propose a prediction method based on epsilon-insensitive support vector machine regression (epsilon-SVR). A brief overview of the theoretical background of this fairly new technique and the use of specific kernel functions based on commonly applied genetic similarity measures for dominant and co-dominant markers are presented. These different marker types can be integrated into a single regression model by means of simple kernel operations. Field trial data from the grain maize breeding programme of the private company RAGT R2n are used to assess the predictive capabilities of the proposed methodology. Prediction accuracies are compared to those of one of today's best performing prediction methods based on best linear unbiased prediction. Results on our data indicate that both methods match each other's prediction accuracies for several combinations of marker types and traits. The epsilon-SVR framework, however, allows for a greater flexibility in combining different kinds of predictor variables.