ReviewJournal of animal science and biotechnology2025
Exploring cattle structural variation in the era of long reads, pangenome graphs, and near-complete assemblies.
Review in Journal of animal science and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- From candidate genes to whole-genome approaches: current insights into the genetic architecture of ovine milk production and quality traits.Veterinary and animal science · 2026Review
- Pan-genomics and multi-omics for deciphering genetic variation and accelerating genetic improvement in ruminant livestock.Functional & integrative genomics · 2026Review
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- Resolving Cattle GWAS Loci: Current Progress, Persistent Challenges and Future Directions.Current issues in molecular biology · 2026Review
- First breed-pool whole genome sequencing of egyptian sheep: a comprehensive genomic atlas revealing diversity and candidate genes for production and adaptation.BMC biotechnology · 2026Article
- Integrative Advances in Equine Genomics From Reference Assemblies to Evolutionary History and Key Traits.Evolutionary applications · 2026Review
- Review
- The Genomic Landscape of Cattle: Domestication, Dispersal, and Adaptive Evolution.Animals : an open access journal from MDPI · 2026Review
- Detection and evaluation of copy number variation using both linked-read and short-read sequencing in New Zealand dairy cattle.Frontiers in genetics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.