Using SNP arrays to leverage historical data sets for improved prediction accuracy and study of G×E×M in peach
Peach breeding is undertaken by a large number of organisations across many countries. However, there is little quantitative information on the interaction of selection/cultivar performance across different environments and production regions that can be used to improve selection of exotic germplasm for local commercial deployment and breeding. Here we propose a novel method that uses SNP array assays available for individuals assessed in different environments to estimate realized relationships. This approach enables data from otherwise disconnected historical data sets to be combined into a single analysis and thereby the stability of genetic performance across environments to be quantified and used to improve selection decisions. This approach is demonstrated for soluble solids content across three locations in USA. Collaborations with international partners are sought to improve the scope of this study and the accuracy of prediction of performance of candidates across environments in which they have not yet been grown.
Hardner, C., Gasic, K., da Silva Linge, C., Worthington, M., Byrne, D., Peace, C. and Iezzoni, A. (2021). Using SNP arrays to leverage historical data sets for improved prediction accuracy and study of G×E×M in peach. Acta Hortic. 1304, 133-140
Prunus persica (L.) Batsch, soluble solids content (SSC), RosBREED, mixed linear models, IPSC 9K peach SNP array v1