Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data.

ABSTRACT: Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G × E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing env

SUBMITTER: Rogers AR 

PROVIDER: S-EPMC9245610 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature