An Assessment of the Factors Influencing the Prediction Accuracy of Genomic Prediction Models Across Multiple Environments.

ABSTRACT: The effects of climate change create formidable challenges for breeders striving to produce sufficient food quantities in rapidly changing environments. It is therefore critical to investigate the ability of multi-environment genomic prediction (GP) models to predict genomic estimated breeding values (GEBVs) in extreme environments. Exploration of the impact of training set composition on the accuracy of such GEBVs is also essential. Accordingly, we examined the influence of the number of training environments and the use of environmental covariates (ECs) in GS models on four subsets of n = 500 lines of the soybean nested association mapping (SoyNAM) panel grown in nine environments in the US-North Central Region. The ensuing analyses provided insights into the influence of both of

SUBMITTER: Widener S 

PROVIDER: S-EPMC8343134 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature