Building a human genetic data lake to scale up insights for drug discovery

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.