In practice, the extraction and interpretation of the relevant biological information from available repositories is not always straightforward, and can be time consuming as well as error prone. We have, therefore, developed FUMA, which functionally annotates GWAS findings and prioritizes the most likely causal SNPs and genes using information from 18 biological data repositories and tools. Gene prioritization is based on a combination of positional mapping, expression quantitative trait loci (eQTL) mapping and chromatin interaction mapping. Results are visualized to facilitate quick insight into the implicated molecular functions. FUMA is available as an online tool at http://fuma.ctglab.nl, where users can customize settings to for example only use exonic SNPs for annotation, or only use SNPs that are eQTLs in specific tissues for the annotation based on expression data. As input, FUMA requires GWAS summary statistics and outputs include multiple tables and figures containing extensive information on, e.g., functionality of SNPs in genomic risk loci, including protein-altering consequences, gene-expression influences, open-chromatin states as well as three-dimensional (3D) chromatin interactions. The online tool includes interactive figures that can be used to explore
