Shahid Bahonar University of Kerman
Abstract: (5 Views)
The growth trait in beef cattle is one of the most important economic indices in the livestock industry, and is controlled by a complex network of genes. This study aimed to identify key genomic regions and gene networks associated with growth in beef cattle using a meta-analysis approach applied to genome-wide association study data. The genomic data were extracted from six published studies between 2010 and 2025 and then analyzed using Stouffer's statistical method. The investigated breeds included Simmental, Angus, Hereford, Charolais, and other beef breeds from China, Russia, the United States, Canada, and Brazil. Meta-analysis results showed that chromosomes 2, 4, 11, 21, 24, and 27 exhibited the most significant signals, and the peak –log10(p) value of 27.62 was found on chromosome 24. Protein-protein interaction network analysis using the STRING database showed four major functional clusters: metabolism and energy, cellular regulation and signaling, transcriptional regulation, and neuromuscular system. GALNTL6, DPP6, KCNIP4, ALDH7A1, SLC27A6, and ABCG5 were identified as key hub genes playing important roles functions in muscle growth and metabolism control. The identified gene network follows a scale-free pattern with moderate density and a high clustering coefficient, showing modular organization and efficient signal transmission. This meta-analysis increased the statistical power compared to individual studies, and some novel genomic regions were thus revealed that had not been previously detected. These findings can be useful in genomic-based selection and breeding programs to improve growth traits in beef cattle.
Article number: 2
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