FUMA is an integrative web-based platform that facilitates functional annotation of genome-wide association study (GWAS) results, gene prioritization, and interactive visualization. It uses information from multiple biological resources to identify likely causal variants and genes associated with traits. FUMA accommodates positional, expression quantitative trait locus (eQTL), and chromatin interaction mappings, and provides gene-based, pathway, and tissue enrichment results. It helps generate hypotheses for functional experiments to confirm causal relationships. FUMA integrates data from 18 biological repositories and tools to process GWAS summary statistics and provide annotations. It includes SNP2GENE and GENE2FUNC processes. SNP2GENE prioritizes functional SNPs and genes based on positional, eQTL, and chromatin interaction data. GENE2FUNC provides gene expression heatmaps, enrichment of differentially expressed genes, and links to external biological information. Results are visualized for quick interpretation. FUMA is applied to GWAS for body mass index (BMI), Crohn's disease (CD), and schizophrenia (SCZ). For BMI, FUMA identified 95 lead SNPs across 77 genomic risk loci, prioritizing 151 genes. For CD, 95 lead SNPs were identified across 71 loci, prioritizing 95 genes. For SCZ, 128 lead SNPs were identified across 109 loci, prioritizing 84 genes. FUMA also identified novel genes outside of risk loci, which were not reported in original studies. These genes showed shared biological functions with known candidates. FUMA provides interactive visualizations and downloadable results, including tables and figures. It integrates multiple lines of evidence and facilitates rapid interpretation of GWAS results. FUMA is a versatile tool that combines state-of-the-art biological data sources to generate hypotheses for functional follow-up analysis. It is available as an online tool at http://fuma.ctglab.nl.FUMA is an integrative web-based platform that facilitates functional annotation of genome-wide association study (GWAS) results, gene prioritization, and interactive visualization. It uses information from multiple biological resources to identify likely causal variants and genes associated with traits. FUMA accommodates positional, expression quantitative trait locus (eQTL), and chromatin interaction mappings, and provides gene-based, pathway, and tissue enrichment results. It helps generate hypotheses for functional experiments to confirm causal relationships. FUMA integrates data from 18 biological repositories and tools to process GWAS summary statistics and provide annotations. It includes SNP2GENE and GENE2FUNC processes. SNP2GENE prioritizes functional SNPs and genes based on positional, eQTL, and chromatin interaction data. GENE2FUNC provides gene expression heatmaps, enrichment of differentially expressed genes, and links to external biological information. Results are visualized for quick interpretation. FUMA is applied to GWAS for body mass index (BMI), Crohn's disease (CD), and schizophrenia (SCZ). For BMI, FUMA identified 95 lead SNPs across 77 genomic risk loci, prioritizing 151 genes. For CD, 95 lead SNPs were identified across 71 loci, prioritizing 95 genes. For SCZ, 128 lead SNPs were identified across 109 loci, prioritizing 84 genes. FUMA also identified novel genes outside of risk loci, which were not reported in original studies. These genes showed shared biological functions with known candidates. FUMA provides interactive visualizations and downloadable results, including tables and figures. It integrates multiple lines of evidence and facilitates rapid interpretation of GWAS results. FUMA is a versatile tool that combines state-of-the-art biological data sources to generate hypotheses for functional follow-up analysis. It is available as an online tool at http://fuma.ctglab.nl.