Genomic Selection in Plant Breeding: A Comparison of Models

ABSTRACT Simulation and empirical studies of genomic selection (GS) show accuracies sufficient to generate rapid genetic gains. However, with the increased popularity of GS approaches, numerous models have been proposed and no comparative analysis is available to identify the most promising ones. Using eight wheat ( Triticum aestivum L.), barley ( Hordeum vulgare L.), Arabidopsis thaliana (L.) Heynh., and maize ( Zea mays L.) datasets, the predictive ability of currently available GS models along with several machine learning methods was evaluated by comparing accuracies, the genomic estimated breeding values (GEBVs), and the marker effects for each model. While a similar level of accuracy was observed for…

• M
  Microsoft
• NI
  National Institute of Food and Agriculture