Genome-wide association studies (GWAS) have identified numerous disease-associated variants, yet efficient storage and analysis of genetic data remain a challenge. Here, we propose a scalable genetic data lake (GDL) integrating GWAS, molecular quantitative trait loci (mQTL), and epigenetic data within a big data infrastructure to enable rapid analysis. This framework allows large-scale computations, prioritizing 54 586 gene–trait associations, including 34 779 found exclusively in consortium data sets. By leveraging public, consortium, and private data, this approach enhances target discovery and indication selection, accelerating drug development.