MegaLMM improves genomic predictions in new environments using environmental covariates.

ABSTRACT: Multienvironment trials (METs) are crucial for identifying varieties that perform well across a target population of environments. However, METs are typically too small to sufficiently represent all relevant environment-types, and face challenges from changing environment-types due to climate change. Statistical methods that enable prediction of variety performance for new environments beyond the METs are needed. We recently developed MegaLMM, a statistical model that can leverage hundreds of trials to significantly improve genetic value prediction accuracy within METs. Here, we extend MegaLMM to enable genomic prediction in new environments by learning regressions of latent factor loadings on Environmental Covariates (ECs) across trials. We evaluated the extended MegaLMM using the maize G

SUBMITTER: Hu H 

PROVIDER: S-EPMC11708919 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature