GENEasso: a curated resource of credible disease–gene associations across complex diseases from GWAS summary statistics

Gene-based association analysis has become a powerful strategy to improve the biological interpretability of genome-wide association studies (GWAS) by aggregating variant-level signals at the gene level. Although several transcriptome-wide association study (TWAS)-specific databases have been developed, TWAS represents only one class of gene-based association methods and relies primarily on expression-mediated effects, whereas other gene-based approaches also play critical roles in identifying disease-associated genes. To address this limitation, we present GENEasso, a comprehensive platform that integrates multiple gene-level statistical frameworks to enable robust exploration of disease-gene associations across complex diseases. GENEasso systematically applies seven representative methods to 8226 curated GWAS summary statistics, generating 716 122 high-confidence disease-gene associations. The platform supports cross-method consensus scoring, tissue-specific enrichment prioritization, and ancestry-stratified analyses across five populations. Results can be interactively explored through Manhattan plots and ontology-based navigation, with full transparency and unrestricted access to data. A web server module allows users to upload their own GWAS summary statistics, select gene-based methods, and benchmark their findings against the GENEasso reference database. Concordance across methods increases the credibility of associations, improving reproducibility and supporting user-defined gene prioritization workflows. GENEasso is freely available as an open-access resource at https://www.geneasso.net.