Genomic language model-based genomic prediction in plant breeding

Genomic prediction based on molecular markers has substantially advanced genomic selection; however, prediction accuracy often plateaus despite continued increases in marker density and methodological refinement.This saturation limits the effective use of available genomic information.The emergence of genomic language models (GLMs) offers a new framework for incorporating richer sequence-based information into genomic prediction, potentially capturing biologically meaningful DNA sequence grammar that is poorly represented by tradit ional marker-based approaches.We conclude that the future of genomic prediction will be shaped not primarily by algorithmic refinement but by the biological expressivity of genomic representations, and that GLMs offer a principled path toward expanding this representational frontier.