Exploring cattle structural variation in the era of long reads, pangenome graphs, and nearly complete assemblies

Authors: Liu, Ge

Submitted to: Journal of Animal Science and Biotechnology
Publication Type: Review Article
Publication Acceptance Date: 10/12/2025
Publication Date: 11/24/2025
Citation: Liu, G. 2025. Exploring cattle structural variation in the era of long reads, pangenome graphs, and nearly complete assemblies. Journal of Animal Science and Biotechnology. 16(1):158. https://doi.org/10.1186/s40104-025-01294-7.
DOI: https://doi.org/10.1186/s40104-025-01294-7

Interpretive Summary: Cattle DNA contains not only small changes but also large differences called structural variations (SVs), which can affect traits like milk production, fertility, disease resistance, and feed efficiency. Until recently, many of these SVs were missed, but new technologies such as long-read sequencing, pangenome graphs, and near-complete genomes now allow scientists to find them more accurately, even in hard-to-study regions. By linking SVs to gene activity and animal performance, researchers can better understand how these variations shape important traits. In the future, combining SV data with other tools and artificial intelligence could help farmers breed healthier and more productive cattle, though challenges like cost and data sharing still need to be solved. This review will be valuable for farmers, scientists, and policymakers focused on enhancing animal health and production through genome-enabled selection.

Technical Abstract: Structural variations (SVs) are a critical but underexplored source of genetic diversity in cattle, shaping traits vital for productivity, adaptability, and health. Advances in long-read sequencing, pangenome graph construction, and near-complete genome assemblies now allow accurate SV detection and genotyping. These innovations overcome the limitations of single-reference genomes, enabling the discovery of complex SVs, including nested and overlapping variants, and providing access to previously inaccessible genomic regions such as centromeres and telomeres. This review highlights the current landscape of cattle SV research, with emphasis on integrating long-read sequencing and pangenome frameworks to uncover breed-specific and population-level variation. While many SVs are linked to economically important traits such as feed efficiency and disease resistance, their broader regulatory impacts remain an active area of investigation. Emerging functional genomics approaches, including transcriptomics, epigenomics, and genome editing, will clarify how SVs influence gene regulation and phenotype. Looking ahead, the convergence of long-read sequencing, multi-omics integration, and artificial intelligence offers new opportunities to incorporate SVs into genomic prediction and precision breeding. At the same time, challenges related to cost, imputation, and scalability underscore the need for collaborative efforts to build open genomic resources that capture the diversity of global cattle populations. Together, these advances position SV research to transform livestock genomics, driving sustainability and resilience in the face of evolving agricultural demands.