Computational Molecular Biology 2025, Vol.15, No.5, 235-244 http://bioscipublisher.com/index.php/cmb 235 Research Insight Open Access Integrating Multi-Omics Data to Explore the Genetic Basis of Milk Production in Dairy Cattle Jingya Li, Jun Li Animal Science Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding author: jun.li@cuixi.org Computational Molecular Biology, 2025, Vol.15, No.5 doi: 10.5376/cmb.2025.15.0023 Received: 19 Jul., 2025 Accepted: 31 Aug., 2025 Published: 23 Sep., 2025 Copyright © 2025 Li and Li, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.6 Preferred citation for this article: Li J.Y., and Li J., 2025, Integrating multi-omics data to explore the genetic basis of milk production in dairy cattle, Computational Molecular Biology, 15(5): 235-244 (doi: 10.5376/cmb.2025.15.0023) Abstract The milk production trait of dairy cows is a complex quantitative trait with high economic value. With the development of high-throughput technology, integrating multi-omics data such as genomics, transcriptomics, epigenomics, proteomics and metabolomics has become an important means to reveal the genetic basis of milk production traits. This study, through a multi-omics integration strategy, explored the core genes and regulatory networks that affect milk production and its related traits, reviewed the main milk production traits and their related quantitative trait loci (QTLS), systematically sorted out the characteristics of various omics data and their integrated analysis methods, such as weighted gene co-expression network analysis (WGCNA) and multi-omics factor analysis (MOFA), the focus is on key genes (such as STAT5A, CSN2) in the breast development and lactation regulatory pathways, the interaction network between miRNA and mRNA, as well as functional enrichment pathways. This study demonstrates the potential of multi-omics integration in analyzing complex traits, providing a scientific basis for promoting the intelligence and efficiency of molecular breeding in dairy cows, and also offering ideas for future functional gene verification and optimization of precise breeding strategies. Keywords Dairy cows; Milk production characteristics; Multi-omics integration; Gene regulatory network; Candidate gene identification 1 Introduction The fact that cows produce milk sounds simple, but the things involved behind it are actually quite complicated. It is not only related to how much money a ranch can earn in a year, but also to the stable development of the global dairy industry and even how we understand the complex traits of mammals (Brito et al., 2021; Ristanic et al., 2024). Making milk more abundant and of better quality has long been the goal of almost all breeding projects. But things are not that easy. Milk production is essentially a polygenic trait, not only controlled by hundreds or even thousands of genes, but also influenced by environmental fluctuations. In the past, most breeding relied on "visible and tangible" phenotypes and family trees to predict the quality of genes. Indeed, this method has greatly increased the overall yield of dairy cows, but there are also many problems, such as low heritability, slow intergenerational turnover, and the most troublesome ones are those genes with small effects and vulnerable to external influences, which are often overlooked. The wind direction has started to change in recent years. The popularization of high-throughput omics - from the genome to the transcriptome, proteome, and then to epigenetics and metabolomics - has brought about new breakthroughs in the study of milk production. Rather than focusing on a single data layer, it is better to conduct multiple omics simultaneously. Only in this way can those variant loci, regulatory switches and pathway networks that truly affect milk production be identified (Nasir et al., 2024; Cai et al., 2025). Some analyses have shown that not only structural genes, but also non-coding Rnas specifically expressed in the mammary gland, epigenetic modifications, and even larger regulatory networks all have a say in milk production and component control. If all these omics layer data are integrated, not only can milk production be predicted more accurately, but the improvement direction will also be clearer (Zheng et al., 2025). This study aims to establish a robust framework for integrating multi-omics data to analyze the genetic and molecular mechanisms of milk production in dairy cows, identify key genetic variations and regulatory elements
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