# Application of qualifying variants for genomic analysis

## TL;DR

This paper introduces a new framework for genomic analysis that treats qualifying variants as dynamic elements to improve transparency and reproducibility.

## Contribution

The paper proposes a unified, portable specification for qualifying variant criteria to enhance genomic analysis workflows.

## Key findings

- Decoupling QV criteria from pipeline code improves clarity and reuse.
- Validation showed QV-based workflows match conventional methods but with greater interpretability.
- The framework supports reproducibility and interdisciplinary communication in genomic analysis.

## Abstract

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. In practice these rules are embedded within pipelines, which hinders transparency, audit, and reuse across tools. A unified, portable specification for QV criteria is needed.

Our aim is to embed the concept of a “QV” into the genomic analysis vernacular, moving beyond its treatment as a single filtering step. By decoupling QV criteria from pipeline variables and code, the framework enables clearer discussion, application, and reuse. It provides a flexible reference model for integrating QVs into analysis pipelines, improving reproducibility, interpretability, and interdisciplinary communication. Validation across diverse applications confirmed that QV based workflows match conventional methods while offering greater clarity and scalability.

The source code and data are accessible at the Zenodo repository https://doi.org/10.5281/zenodo.17414191. Manuscript files are available at https://github.com/DylanLawless/qvApp2025lawless. The QV framework is available under the MIT licence, and the dataset will be maintained for at least two years following publication.