Dairy milk production is a quantitative trait that is controlled by many biological and environmental factors. This study employs a network-driven systems approach and clustering algorithm to uncover deeper insights into its genetic associations. We analyzed the GSE33680 dataset from the GEO database to understand the biological importance of milk production through gene expression and modules. In this study, we employed CytoNCA and ClusterONE plugins within Cytoscape for network analysis. Moreover, miRWalk software was utilized to detect miRNAs, and DAVID was employed to identify gene ontology and pathways. The results revealed 140 up-regulated genes and 312 down-regulated genes. In addition, we have identified 91 influential genes and 47 miRNAs that are closely associated with milk production. Through our examination of the network connecting these genes, we have found significant involvement in important biological processes such as calcium ion transit across cell membranes, the BMP signaling pathway, and the regulation of MAPK cascade. The conclusive network analysis further reveals that GAPDH, KDR, CSF1, PYGM, RET, PPP2CA, GUSB, and PRKCA are closely linked to key pathways essential for governing milk production. Various mechanisms can control these genes, making them valuable for breeding programs aiming to enhance selection indexes.
Keywords: gene ontology; miRNA identification; milk production; network analysis.