Application of qualifying variants for genomic analysis
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Motivation
Qualifying variants (QVs) are genomic alterations selected by defined criteria within analysis pipelines. Although crucial for both research and clinical diagnostics, QVs are often seen as simple filters rather than dynamic elements that influence the entire workflow. While best practices follow variant classification standards and standardised workflows, a unified framework to integrate and optimise QVs for advanced applications is missing.
Results
Our aim is to embed the concept of a “QV” into the genomic analysis vernacular, moving beyond a single filtering step. By decoupling QV criteria from other pipeline variables and code, our approach facilitates easier discussion and application. Our framework, with its new terminology and reference model, offers a flexible approach for integrating QVs into analysis pipelines, thereby enhancing reproducibility, interpretability, and interdisciplinary communication. A validation case study implementing ACMG criteria in a disease cohort shows that our approach matches conventional methods while offering improved clarity and scalability.
Availability
The source code and data are accessible at https://github.com/DylanLawless/qv2025lawless . The QV file used in this work is available from https://doi.org/10.5281/zenodo.15105594 (qv_acmg_svnindel_criteria_20250225.yaml). The QV framework is available under the MIT licence, and the dataset will be maintained for at least two years following publication.
