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ShinyDisVar: a web application for identifying disease-associated variants using large-scale personal genetic information

bioinformatics
computational-biology
medical-genetics

Abstract

Background. Existing web-based tools for identifying disease-associated genetic variants often struggle with scalability and limited database coverage. Many of them lack support for whole-genome variant calling format (VCF) files and are not compatible with the Genome Reference Consortium Human Build 38 (GRCh38), reducing their usability in routine genetic analysis and research.

Methods. We developed ShinyDisVar, a web application built with the R Shiny framework and powered by the DisVar R package. It supports large-scale variant analysis by accepting VCF files and querying six integrated disease-related variant databases: GWAS Catalog, GWASdb, GRASP, GADCDC, Johnson and O'Donnell Database, and ClinVar. All databases were mapped to the GRCh 38 genome assembly.

Results. Benchmarking was conducted using whole-genome VCF files from ten individuals in the 1000 Genomes Project, each containing 3.87 to 4.74 million variants. ShinyDisVar completed variant reading and analysis within 42 to 59 seconds per sample. Validation tests demonstrated 100% sensitivity and 100% specificity when comparing outputs to known pathogenic and negative control datasets. The platform supports up to 9.5 million variants per file and delivers results in both tabular and graphical formats, allowing users to explore disease associations interactively and export results for further analysis.

Conclusions. ShinyDisVar addresses the limitations of current web tools by supporting whole-genome VCF input, integrating multiple curated disease databases, and providing fast, accurate , and accessible variant analysis. It enables researchers and clinicians to identify disease-associated variants without requiring local computational infrastructure or programming expertise . ShinyDisVar is freely available as a user-friendly solution for large-scale genomic variant interpretation in both research and clinical contexts.

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