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Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality.

Rugang TianMaryam MahmoodiJing TianSina Esmailizadeh KoshkoiyehMeng ZhaoMahla SaminzadehHui LiXiao WangYuan LiAli Esmailizadeh
Published in: Genes (2024)
Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.
Keyphrases
  • genome wide
  • gene expression
  • single cell
  • mass spectrometry
  • quality improvement
  • dna methylation
  • copy number
  • healthcare
  • machine learning
  • climate change
  • genome wide association
  • transcription factor
  • human health