Genomic selection for herbage yield in forage oats (Avena sp.)

Abstract

The study investigated the potential of genomic selection (GS) to accelerate genetic improvement in forage oats (Avena sp.) by predicting herbage yield. The results showed that GS can be an effective tool for predicting herbage yield in forage oats, with prediction accuracies ranging from 0.91 to 0.97. The use of SNP markers for GS in forage oats has several advantages over traditional marker-assisted selection (MAS), including the ability to capture more of the genetic variation for the trait of interest. The accuracy of GS predictions can be further improved by using trait-specific relationship matrices (TGRMs) and genomic information from multiple generations. Key FindingsO_LIGS can be an effective tool for predicting herbage yield in forage oats, with prediction accuracies ranging from 0.91 to 0.97. C_LIO_LIThe use of SNP markers for GS in forage oats has several advantages over traditional MAS, including the ability to capture more of the genetic variation for the trait of interest. C_LIO_LIThe accuracy of GS predictions can be further improved by using TGRMs and genomic information from multiple generations. C_LI ImplicationsO_LIGS can be used to accelerate the development of new forage oat varieties with improved herbage yield. C_LIO_LIGS has the potential to significantly improve the agronomic performance and quality of forage oat varieties. C_LI Future ResearchO_LIDevelop a more structured training population to improve the accuracy of GS predictions. C_LIO_LIIdentify trait-specific relationship matrices (TGRMs) to further improve the accuracy of GS predictions. C_LIO_LIInvestigate the use of genomic information from multiple generations to improve the accuracy of GS predictions. C_LI