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利用功能基因组学理解牛肉质量的复杂性,并培育具有改善肉质的肉牛。

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

机构信息

Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China.

Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman P.O. Box 76169-133, Iran.

出版信息

Genes (Basel). 2024 Aug 22;15(8):1104. doi: 10.3390/genes15081104.

Abstract

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.

摘要

消费者对牛肉的看法受到整体肉质的极大影响,这是牛肉行业的一个关键因素。基因组学有可能通过基因组选择(GS)和全基因组关联研究(GWAS)方法来改良重要的牛肉质量性状,并鉴定与这些性状相关的遗传标记和因果变异。通过识别与质量性状相关的差异表达基因、蛋白质和代谢途径,转录组学、蛋白质组学和代谢组学为潜在的遗传机制提供了深入的见解,补充了 GWAS 数据。利用这些功能基因组学技术可以优化肉牛的选育,以提高质量性状,满足高质量牛肉的需求。本文全面概述了组学技术在揭示牛肉质量复杂性的功能变异方面的应用现状。通过突出 GWAS、GS、转录组学、蛋白质组学和代谢组学研究的最新发现,这项工作旨在为深入了解遗传、基因表达、蛋白质动态和代谢途径在塑造牛肉质量方面的复杂关系提供有价值的资源。

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本文引用的文献

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