A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies

While genomic selection (GS) began revolutionizing plant breeding when it was proposed around 20 years ago, its practical implementation is still challenging as many factors affect its accuracy. One such factor is the choice of the statistical machine learning method. For this reason, we explore the tuning process under a multi-trait framework using the Gaussian kernel with a multi-trait Bayesian Best Linear Unbiased Predictor (GBLUP) model. We explored three methods of tuning (manual, grid search and Bayesian optimization) using 5 real datasets of breeding programs. We found that using grid search and Bayesian optimization improve between 1.9 and 6.8% the prediction accuracy regarding of using manual tuning. While the improvement in prediction accuracy in some cases can be marginal, it is very important to carry out the tuning process carefully to improve the accuracy of the GS methodology, even though this entails greater computational resources.

Automated summary

Genomic selection is an important technique in plant breeding, but its practical implementation is still challenging. This study explores the tuning process using a Gaussian kernel and a multi-trait Bayesian Best Linear Unbiased Predictor model. Three tuning methods were compared, and it was found that using grid search and Bayesian optimization can improve prediction accuracy.