Beef quality is a crucial factor affecting both consumer preferences and the economic efficiency of the industry. With the rapid advancements in high-throughput technologies, including genomics, transcriptomics, proteomics, and metabolomics, integrated multi-omics analysis has emerged as a new research paradigm for deeply investigating the mechanisms underlying beef quality. This review systematically summarizes recent progress in multi-omics research related to beef quality, encompassing various levels such as genomics, transcriptomics, proteomics, metabolomics, and phenomics. At the genomic level, the use of genome-wide association studies (GWAS) and genomic selection techniques has markedly improved the precision of selecting meat quality traits. Studies in transcriptomics and proteomics have identified key genes involved in muscle growth and fat deposition, along with their expression regulation networks. Metabolomics analyses have highlighted critical metabolites that influence beef flavor and tenderness, as well as their biosynthetic pathways. The integration of multi-omics data has led to the construction of a comprehensive regulatory network linking genotype to phenotype, providing a theoretical foundation for precision breeding and quality control. However, current research faces challenges such as limited sample sizes and the need for more advanced data integration methods. Future research should prioritize: (1) increasing sample sizes and conducting large-scale omics data collection across diverse breeds and environmental conditions; (2) developing sophisticated computational methods for deeper integration of multi-omics data to create more accurate quality prediction models, and (3) enhancing functional validation experiments to elucidate the roles of key genes and metabolites. This review offers a systematic perspective on the molecular mechanisms driving beef quality and is of significant importance for guiding precision breeding and quality control in the beef industry.
