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; Joseph Glessner; Hakon Hakonarson

doi:10.1186/s13059-026-04189-6

PubMed · 42432789

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·Joseph Glessner·Hakon Hakonarson

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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.

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Humans DNA Copy Number Variations Genetic Predisposition to Disease Computational Biology Genome-Wide Association Study Journal Article Review

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Amir Hossein Saeidian, Hani Sabaie, Mahdi Akbarzadeh, Hassan Vahidnezhad, Leila Youssefian, Toktam Saadattalab, Haowei Du, Xi Luo, Nichole Marie Owen, Xiaonan Zhao, Joseph Glessner, Hakon Hakonarson. 2026-07-10. Computational strategies for copy number variation detection, disease association, and beyond.. https://doi.org/10.1186/s13059-026-04189-6

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