Prediction of maize single-cross hybrid performance: support vector machine regression versus best linear prediction.
Accurate prediction of the phenotypic performance of a hybrid plant based on the molecular fingerprints of its parents should lead to a more cost-effective breeding programme as it allows to reduce the number of expensive field evaluations. The construction of a reliable prediction model requires a representative sample of hybrids for which both molecular and phenotypic information are accessible. This phenotypic information is usually readily available as typical breeding programmes test numerous new hybrids in multi-location field trials on a yearly basis. Earlier studies indicated that a linear mixed model analysis of this typically unbalanced phenotypic data allows to construct epsilon-insensitive support vector machine regression and best linear prediction models for predicting the performance of single-cross maize hybrids. We compare these prediction methods using different subsets of the phenotypic and marker data of a commercial maize breeding programme and evaluate the resulting prediction accuracies by means of a specifically designed field experiment. This balanced field trial allows to assess the reliability of the cross-validation prediction accuracies reported here and in earlier studies. The limits of the predictive capabilities of both prediction methods are further examined by reducing the number of training hybrids and the size of the molecular fingerprints. The results indicate a considerable discrepancy between prediction accuracies obtained by cross-validation procedures and those obtained by correlating the predictions with the results of a validation field trial. The prediction accuracy of best linear prediction was less sensitive to a reduction of the number of training examples compared with that of support vector machine regression. The latter was, however, better at predicting hybrid performance when the size of the molecular fingerprints was reduced, especially if the initial set of markers had a low information content.
Maenhout
Steven
S
Department of Biosciences and Landscape Architecture, University College Ghent, Voskenslaan 270, 9000 Gent, Belgium. Steven.Maenhout@hogent.be
De Baets
Bernard
B
Haesaert
Geert
G
D001185:Artificial Intelligence; D005819:Genetic Markers; D006823:Hybrid Vigor; D006824:Hybridization, Genetic; D016014:Linear Models; D008957:Models, Genetic; D010641:Phenotype; D012044:Regression Analysis; D003313:Zea mays / Q000235:genetics*
D005819:Genetic Markers