Prediction of single-cross performance in a hybrid breeding program is extremely important because it is not feasible to evaluate all parental combinations. Recent simulation and field studies have shown great promise of genomic prediction of single-cross performance. These previous studies, however, have primarily focused on parametric genomic prediction models. This study tested three nonparametric models—reproducing kernel Hilbert spaces, support vector regression, and neural networks— for prediction of early-stage single crosses. Two separate datasets, consisting of 481 and 312 single crosses, were used to evaluate models. Single crosses were made by randomly crossing inbred progenies between heterotic groups. Genomic prediction models were trained to directly predict single-cross performance, or to predict general combining ability (GCA) of inbred parents and specific combining abilities (SCA) of single crosses between them. Using cross-validation, genomic predictions were compared with predictions using phenotypes of single crosses with a common parent (common-parent single crosses), as well as phenotypic estimates of GCA. Of these three options for predicting singlecross performance, genomic prediction resulted in the highest correlation between observed and predicted values. Predictive abilities of parametric and nonparametric genomic prediction models were nearly identical. All genomic prediction models displayed good ability to predict GCA effects, but none could predict SCA effects. Our results suggest that nonparametric models do not provide an advantage over parametric models for prediction of early-stage, singlecross performance using modestly sized training populations like those used here.
Bibliographical noteFunding Information:
This research was partially supported by a grants from the Nebraska Corn Board and National Research Initiative or Agriculture and Food Research Initiative Competitive Grants Program (Grant no. 2012-67013-19460) from the USDA National Institute of Food and Agriculture. D.C. Kadam was supported by an Indian Council of Agricultural Research International Fellowship. We are indebted to Ronal Haarman of Dupont Pioneer for contributing phenotypic data to Dataset I, as well as to Rex Bernardo of the University of Minnesota for helpful suggestions that enhanced the accuracy and clarity of the final manuscript.
© 2019 The Author(s).