Exploring Cattle Structural Variation in the Era of Long Reads, Pangenome Graphs, and near-Complete Assemblies
Open accessAbstract
Structural variations (SVs ≥ 50 bp) 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 forward, the integration of SV catalogs with multi-omics data, imputation resources, and artificial intelligence-driven models will be essential for translating discoveries into breeding and conservation applications. Integrating structural variants into breeding pipelines promises to revolutionize livestock genomics, enabling precision selection and sustainable agriculture despite challenges in cost, data sharing, and functional validation.
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Graph pangenomes represent genomes as network structures, allowing for the simultaneous storage of multiple haplotypes and complex nested variations.
“Unlike linear concatenations, graph pangenomes represent genomes as network structures (nodes and edges), allowing for the simultaneous storage of multiple haplotypes and complex nested variations [183,184].”
From The Genomic Landscape of Cattle: Domestication, Dispersal, and Adaptive Evolution (2026) · cites this paper as [183]
The Pangenome graphs section describes shared sequences as graph nodes, alternative haplotypes as paths, and structural variants as bubbles. It supports representing multiple haplotypes and types of variation in one graph, but does not specifically address nested variations. (LLM, conf=0.85)
Experimental — accuracy not yet measuredGraph pangenomes, facilitated by third-generation long-read sequencing, effectively resolve highly repetitive regions and centromeres.
“Graph pangenomes, facilitated by third-generation long-read sequencing, effectively resolve highly repetitive regions and centromeres [183].”
From The Genomic Landscape of Cattle: Domestication, Dispersal, and Adaptive Evolution (2026) · cites this paper as [183]
The Pangenome graphs section (page unavailable) says graph frameworks improve variant discovery and genotyping in repetitive and structurally complex regions, in the context of long-read data. The Advances in genome assemblies section (page unavailable) attributes resolution of centromeres to nearly complete assemblies, not specifically to graph pangenomes, so that part of the claim is unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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Telomere-to-telomere sheep genome assembly identifies variants associated with wool fineness
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Pangenome-genotyped structural variation improves molecular phenotype mapping in cattlePreprint
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Flagged first, then by citations
Research works printed but not linked: 1
- [65]Schloissnig S, Pani S, Rodriguez-Martin B, Ebler J, Hain C, Tsapalou V, et al. Structural variation in 1,019 diverse humans based on long-read sequencing. Nature. 2025;644:442–52. 10.1038/s41586-025-09290-7. 40702182 PMC12350158 · DOI
Cited by
Sorted by citations
The Genomic Landscape of Cattle: Domestication, Dispersal, and Adaptive Evolution
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- Authors
- Liu, G. E.
- Affiliations
- Agricultural Research Service (US)
- Corresponding author
- Liu, George E.
- ORCID
- Liu, George E.
- Venue
- Journal of Animal Science and Biotechnology
- Published
- 2025-11-24
- Volume
- 16
- Issue
- 1
- Pages
- 158-158
- ISSN
- 1674-9782 (ISSN-L)
- 2049-1891
- PMC
- PMC12641989
- OpenAlex
- W4416563578
- Cited by
- 8
- References
- 89
- Citations by year
- 2026: 8
- FWCI
- 1.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of Food and Agriculture (2019-67015-29321, 2021-67015-33409)
- Agricultural Research Service (0500-00093-001-00-D)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- cattle, structural variants, long-read sequencing, pangenome graphs, feed efficiency, disease resistance, gene expression regulation, livestock breeding, cattle conservation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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