Evidence map›Paper›PMID 42432789›Full record

ReviewGenome biology2026

Computational strategies for copy number variation detection, disease association, and beyond.

Amir Hossein Saeidian, Hani Sabaie, Mahdi Akbarzadeh, Hassan Vahidnezhad, Leila Youssefian, Toktam Saadattalab, Haowei Du, Xi Luo, Nichole Marie Owen, Xiaonan Zhao and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Amir Hossein Saeidian *Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Hani Sabaie *Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Mahdi Akbarzadeh *Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Molecular Biology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hassan VahidnezhadCenter for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Leila YoussefianCenter for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Toktam SaadattalabDepartment of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran.
Haowei DuDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Xi LuoDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Nichole Marie OwenDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Xiaonan ZhaoDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Joseph Glessner *Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA. glessner@chop.edu.
Hakon Hakonarson *Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA. hakonarson@chop.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Copy number variations (CNVs) are key structural variations that contribute to human genetic diversity, evolution, and disease susceptibility. Advances in sequencing technologies and computational methods have improved CNV detection, yet association studies remain challenged by methodological limitations and a lack of standardisation. This review provides an overview of computational strategies for germline CNV detection and disease association. We highlight the value of CNV analysis for uncovering genetic contributions to complex traits and disease risk and outline an analysis workflow including key benchmarking methods. We also discuss current challenges and future directions for advancing CNV detection and association analysis.

Indexed as

Computational BiologyDNA Copy Number VariationsGenetic Predisposition to DiseaseGenome-Wide Association StudyHumans

Identifiers

PMID42432789
PMCPMC13551817

What OpenQuestion holds

Textmetadata
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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 OpenQuestion graph.