Analyzing Cross-Trait Genetic Architecture with the BIGA Cloud Computing Platform

As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this article, we introduce BIGA (https://bigagwas.org/), a website that aims to offer unified data analysis pipelines and processed data resources for cross-trait genetic architecture analyses. We have curated over 15,000 phenotypes and integrated four statistical genetics tools to support cloud-based batch-running for various cross-trait data analyses. Additionally, we developed a novel online query function for GWAS summary statistics, linking to over 30,000 harmonized datasets in the GWAS Catalog. Through BIGA, users can upload their own data, query data online without local downloads, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.