Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data.

ABSTRACT: Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Association (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes and (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-

SUBMITTER: Costa-Neto G 

PROVIDER: S-EPMC9911085 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature