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GWASHub: An Automated Cloud-Based Platform for Genome-Wide Association Study Meta-Analysis
Medrxiv : the Preprint Server for Health Sciences
|November 24, 2025
Summary
GWASHub is a secure, cloud-based platform that streamlines the complex process of genome-wide association studies (GWAS) meta-analysis. It automates data handling, quality control, and analysis, enabling researchers to accelerate genetic discovery.
Area of Science:
- Genetics and Genomics
- Bioinformatics
- Computational Biology
Background:
- Genome-wide association studies (GWAS) meta-analyses aggregate data from millions of participants across cohorts to increase statistical power for genetic discovery.
- The growing volume of genomic biobanks and focus on detailed analyses necessitate scalable infrastructures for data handling, quality control (QC), and meta-analysis.
- Existing workflows often present challenges in managing numerous summary statistic files and associated metadata, increasing the burden on researchers.
Purpose of the Study:
- To develop a secure, cloud-based platform, GWASHub, for efficient curation, processing, and meta-analysis of GWAS summary statistics.
- To provide a user-friendly interface and robust backend infrastructure to manage large-scale GWAS data and workflows.
- To enhance reproducibility and reduce errors in GWAS meta-analysis, facilitating downstream research and clinical translation.
Main Methods:
- Development of GWASHub, a secure cloud-based platform utilizing AWS MySQL database and S3 block storage.
- Implementation of features including private project spaces, automated file harmonization and data validation, metadata capture, customizable variant QC, and meta-analysis.
- User interaction via an intuitive web interface built on Nuxt.js, with scalable analysis job distribution to AWS compute resources.
Main Results:
- GWASHub provides automated QC dashboards for manual review and a meta-analysis module for processed datasets.
- Secure project spaces facilitate collaboration among researchers with different roles within a consortium.
- Individual datasets and meta-analysis results are downloadable by authorized users, ensuring data accessibility.
Conclusions:
- GWASHub addresses a critical need for a scalable, secure, and user-friendly platform for large-scale GWAS meta-analyses.
- The platform's flexible architecture supports ongoing development and incorporation of new QC and meta-analysis procedures.
- By simplifying complex data management, GWASHub aims to accelerate insights into the genetic architecture of complex traits.
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