BIGA provides different tools for quantifying cross-trait genetic architectures, such as genome-wide genetic correlation methods and local genetic correlation analysis.

BIGA GWAS

To enable efficient data analysis, we have aggregated and preprocessed GWAS summary statistics from different sources (e.g., the UK Biobank, PGC, GWAS Catalog, FinnGen, Biobank Japan, BIG-KP, UKB-PPP, CHIMGEN, and UKB Oxford Brain Imaging Team) and provided curated datasets. Our framework can easily be extended to incorporate additional methods and GWAS summary statistics. We plan to update our databases to include new data recourses every six months.

The User can either upload your own data or directly query data from publicly available GWAS data resources (e.g., GWAS Catalog, IEU OpenGWAS project, and Neale lab), BIGA will harmonize user's input summary statistics data, which can also be downloaded after job is finished.

Currently 1001 curated GWAS from 9 studies, 3203 queryable GWAS from the GWAS Catalog, and 5072 queryable GWAS from the IEU OpenGWAS.

If you have any suggestions, bug reports or other questions for BIGA GWAS, you can let us know through BIGA GWAS on our Google Forum: BIGA GWAS

Updates

  • 2026-11-06BIGA has been upgraded to version 1.2.0.
    Faster job pages and downloads, more reliable handling of jobs that run at the same time, and several bug fixes.
  • 2025-07-10BIGA has been upgraded to version 1.1.4.
    New features include an updated harmonization-pipeline flowchart and user-sharing guidance.
  • 2024-07-12BIGA has been upgraded to version 1.1.3.
    New features include an updated harmonization pipeline and upgraded logging system for checking input data.
    Details on categorizing input data errors can be found here: INFO.
  • 2024-03-03BIGA has been upgraded to version 1.1.2.
    Our platform is able to run 20 jobs simultaneously.
    New features include expanded datasets for LAVA analysis (Neale Lab, FinnGen, UKB Oxford, BIG-KP, UKB-PPP Proteins, GWAS Catalog).
  • 2024-02-07BIGA has been upgraded to version 1.1.1.
    New features include customized options for BIGA analysis. Add the CHIMGEN dataset for LDSC, SumHer, and Popcorn analysis.

Detailed information about our updates is available on the Updates page.

Citation

Analyzing cross-trait genetic architecture with the BIGA cloud computing platform.

Yujue Li et al. Journal of the American Statistical Association, 121(554), 929–937. (2026).

https://doi.org/10.1080/01621459.2026.2615068