Optimizing fully-efficient two-stage models for genomic selection using open-source software.

ABSTRACT: Genomic-assisted breeding has transitioned from theoretical concepts to practical applications in breeding. Genomic selection (GS) predicts genomic breeding values (GEBV) using dense genetic markers. Single-stage models predict GEBVs from phenotypic observations in one step, fully accounting for the entire variance-covariance structure among genotypes, but face computational challenges. Two-stage models, preferred for their simplicity and efficiency, first calculate adjusted genotypic means accounting for spatial variation within each environment, then use these means to predict GEBVs. However, unweighted (UNW) two-stage models assume independent errors among adjusted means, neglecting correlations among estimation errors. Here, we show that fully-efficient two-stage models perform similar

SUBMITTER: Fernandez-Gonzalez J 

PROVIDER: S-EPMC11796230 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature