Computational Molecular Biology 2025, Vol.15, No.5, 235-244 http://bioscipublisher.com/index.php/cmb 241 biomarkers also provide a clear direction for marker-assisted selection, enabling breeding to no longer rely solely on phenotypes but to preferentially target those gene loci with a clear functional background (Wadood et al., 2025). Of course, some regulatory variations can easily be overlooked if only relying on traditional SNP models. 7.2 Construction of precision breeding models based on multi-omics support Having data alone is not enough; the key lies in using it skillfully. Integrating the transcriptome, proteome and metabolome into breeding models is no longer a novelty, but the effectiveness still depends on the models themselves. Methods like MultiBLUP, BayesRC and kernel function have demonstrated higher predictive power than traditional genomic selection in multiple studies (Liang et al., 2022). The advantage of these models lies in their ability to handle network hierarchies, functional annotations, and even complex interactions between genes and the environment (Cai et al., 2025). In some groups, personalized breeding strategies have also gradually been put on the agenda, such as customized breeding of different varieties, or improving efficiency in combination with genome editing and assisted reproduction technologies (Xiong et al., 2025). 7.3 Data sharing across populations and development of breeding platforms Scattered data and inconsistent standards are a major bottleneck restricting the wide application of multi-omics. Especially in small groups or non-mainstream varieties, information sparsity often directly affects the performance of the prediction model. For this reason, promoting cross-group data integration becomes particularly important. Multi-variety reference panels and open shared databases have been proven to significantly improve generalization ability (Jin et al., 2024). But to truly bring these data to life, it still relies on cloud platforms, open-source tools and unified formats. These technologies enable real-time connectivity of research data and make the transformation process from research to actual breeding smoother (Gong et al., 2022; Amin et al., 2025). As the platform architecture gradually matures, the threshold for multi-omics integration and meta-analysis is also decreasing. 8 Conclusion and Outlook To understand how milk is produced and why its components are different, merely looking at genes is far from enough. Nowadays, it is generally agreed that multi-omics integration is a more reliable approach. From the genomic and transcriptomic levels all the way to epigenetics, proteins, and metabolism, it can dig deeper into the key points related to the lactation performance of dairy cows (for example, genes like EFNA1, DGAT1, and GLYCAM1 are often not the "main characters" that can be seen at first glance). When the expression genes specific to the breast, the regulatory factors activated only during lactation, and even the epigenetic markers are all incorporated into the model, the estimation of breeding values is no longer a "guess". In other words, with precision breeding truly having a handle, there will be a solid basis for improving the quality and yield of milk. However, one should not speak too confidently. Technology has indeed advanced, but when it comes to actually implementing it, multi-omics is not a "panacea". First of all, the data is mixed and the format is disordered. If not careful during integration, false positive results will pop up. Statistical analysis must keep up. As for the sample size, small-sample studies still account for a considerable proportion, and the truly differentially expressed genes that can be discovered are limited, which naturally reduces the generalisability. The lack of uniformity in sequencing platforms and the differences in experimental batches can be quite annoying due to batch effects, especially when comparing across teams and projects. Integration often fails to work together. Another practical issue: burning money. High-throughput omics technology is costly and requires supporting equipment and professional talents, which small breeding farms cannot afford. Not to mention that some omics levels have not been given due attention at all, such as metabolomics, proteomics and microbiome. Although they also have an impact on the composition of milk, the results are often overlooked. How should the future be headed? Many people actually have a clear idea in their hearts, but to truly implement it, it still needs to be gradually advanced from several aspects. Functional verification of candidate genes and regulatory elements cannot be omitted, and neither wet laboratories nor animal models can be missing. Single-cell omics has also emerged in recent years. It enables us to observe the "division of labor" of different cells in the mammary gland under different conditions, which was unimaginable in the past. Another issue is the tools. The
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