Cloud‐Based GWAS Platform: An Innovative Solution for Efficient Acquisition and Analysis of Genomic Data
Xiaohong Ke, Kailai Li, Aimin Jiang, Yasi Zhang, Qi Wang, Zhengrui Li, Jian Zhang, András Hajd, Wenjie Shi, Ulf D. Kahlert, Anqi Lin, Pengpeng Zhang, Peng Luo
Abstract
ABSTRACT Genome‐wide association studies (GWAS) have identified over 50,000 disease‐associated genetic variants, yet traditional data acquisition and analysis workflows face critical limitations, including inefficient terabyte‐scale data downloading, prohibitive computational infrastructure requirements, and complex cross‐database integration challenges, that impede research accessibility and clinical translation. We developed a cloud‐based GWAS platform integrating data from major international databases (GWAS Catalog, UK Biobank, and FinnGen) encompassing 40,000+ phenotypes across neuroimaging, proteomics, microbiome, metabolomics, and immunology. The platform employs a Kubernetes‐based distributed architecture with hybrid storage systems and optimized indexing structures. We complemented this with FastGWASR, an R package providing seamless integration for Mendelian randomization, drug target validation, and multiomics analyses. Performance evaluations demonstrated second‐level data extraction capabilities with > 99% reduction in local storage requirements and significant hardware demand reduction compared to traditional methods. The platform successfully processed large‐scale analyses, including MR‐PheWAS studies and multiomics integration workflows. Case studies validated platform effectiveness in metabolite‐diabetes causal analysis, PCSK9 drug target validation, and gut microbiome‐inflammation network analysis, achieving comparable scientific accuracy with dramatically improved efficiency. This cloud‐based ecosystem addresses fundamental barriers in GWAS research by democratizing access to genomic data analysis capabilities. The platform's integration of comprehensive data resources with user‐friendly analytical tools accelerates genomic discovery translation into precision medicine applications, particularly benefiting resource‐limited institutions and facilitating collaborative research across disciplines.