BIGA GWAS

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

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

Updates

2024-07-12: BIGA 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.

2024-03-03: BIGA 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).

2024-02-07: 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. Detailed information about our updates is available on the Updates page.

Citation

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

Yujue Li et al. bioRxiv 2023.04.28.538585; doi: https://doi.org/10.1101/2023.04.28.538585




Developed by :
Yujue Li (li4476@purdue.edu)
Bingxin Zhao (bxzhao@wharton.upenn.edu)
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