Multi‐Omics‐Augmented GWAS for Crop Improvement: From Mechanisms to Breeding

ABSTRACT Global food security demands innovative strategies to enhance crop resilience and productivity amidst escalating environmental pressures. While genome‐wide association studies (GWAS) have proven powerful in identifying genetic variants underlying complex agronomic traits, their resolution is often limited by the polygenic nature of traits and genotype‐by‐environment (G × E) interactions. The integration of multi‐omics technologies—including genomics, transcriptomics, proteomics, and metabolomics—with GWAS provides a transformative framework to decipher the molecular mechanisms governing stress adaptation and growth regulation. This review critically examines the synergistic potential of multi‐omics‐augmented GWAS in elucidating genetic architectures, uncovering candidate genes, and reconstructing regulatory networks. We highlight computational and experimental strategies for data integration, address persistent challenges such as polygenic trait dissection and environmental contextualisation, and discuss emerging opportunities through single‐cell omics and machine learning. This multi‐omics‐augmented approach significantly boosts GWAS resolution to uncover candidate genes and reconstruct regulatory networks, thereby addressing the persistent challenges of polygenic trait dissection and environmental contextualisation.