{Reference Type}: Preprint
{Title}: Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform.
{Author}: Li Y;Xue F;Li B;Yang Y;Fan Z;Shu J;Yang X;Wang X;Lin J;Copana C;Zhao B;
{Journal}: bioRxiv
{Volume}: 0
{Issue}: 0
{Year}: 2024 Mar 16
暂无{DOI}: 10.1101/2023.04.28.538585
{Abstract}: 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 paper, 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 using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.