Evidence map›Paper›PMID 41287010›Full record

ReviewJournal of animal science and biotechnology2025

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

George E Liu

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

George E LiuAnimal Genomics and Improvement Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA. george.liu@usda.gov.ORCID http://orcid.org/0000-0003-0192-6705

Funding

Agricultural Research Service 0500-00093-001-00-DNational Institute of Food and Agriculture 2019-67015-29321National Institute of Food and Agriculture 2021-67015-33409
6 · The paper itself

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

CattleGenome assemblyGenomic predictionLong read sequencingPangenome graphStructure variation

Identifiers

PMID41287010
PMCPMC12641989

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.