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

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.

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


2024-02-07: BIGA 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.

2024-01-07: BIGA has been upgraded to version 1.1.0.

New features include a data harmonization step for input summary statistics data. Supported analysis methods now expanded to include LDSC, SumHer, LAVA, and Popcorn.

2023-03-07: The first version (1.0.0) of BIGA was officially released.

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


Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform

Yujue Li, Fei Xue, Bingxuan Li, Yilin Yang, Zirui Fan, Xiaochen Yang, Juan Shu, Xiyao Wang, Jinjie Lin, Carlos Copana, Bingxin Zhao.

bioRxiv 2023.04.28.538585; doi:

Developed by :
Yujue Li (
Bingxin Zhao (
Copyright @ 2023-2024