Functional Mapping and Annotation of Genome-wide association studies

FUMA GWAS

Functional Mapping and Annotation of Genome-Wide Association Studies

About FUMA
FUMA is a platform that can be used to annotate, prioritize, visualize and interpret GWAS results.
The SNP2GENE function takes GWAS summary statistics as an input, and provides extensive functional annotation for all SNPs in genomic areas identified by lead SNPs.
The GENE2FUNC function takes a list of gene IDs (as identified by SNP2GENE or as provided manually) and annotates genes in biological context
To submit your own GWAS, login is required for security reason. If you have't registered yet, you can do from here.
You can browse public results of FUMA (including example jobs) from Browse Public Results without registration or login.

Please post any questions, suggestions and bug reports on Google Forum: FUMA GWAS users.

News
12 May 2023: FUMA is upgraded to version 1.5.4.
User roles and permissions were added for resource management purposes.
06 April 2023: FUMA is upgraded to version 1.5.3.
Registration vulnerability is fixed. Registration with invalid email addresses is not allowed anymore.
26 February 2023: FUMA is upgraded to version 1.5.2.
A frequently asked questions page is added. In addition, some other minor updates to wording on the website was done.
03 February 2023: FUMA is upgraded to version 1.5.1.
Starting from FUMA version 1.5.1, as default MAGMA is unchecked. If you want to obtain results from MAGMA, please select 'Perform MAGMA' in step 6 on the submission page.
For other updates, please see the Updates page.

Citation
When using FUMA, please cite the following.
K. Watanabe, E. Taskesen, A. van Bochoven and D. Posthuma. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8:1826. (2017).
links https://www.nature.com/articles/s41467-017-01261-5
When using cell type analysis, please cite the following.
K. Watanabe, M. Umicevic Mirkov, C. de Leeuw, M. van den Heuvel and D. Posthuma. Genetic mapping of cell type specificity for complex traits. Nat. Commun. 10:3222. (2019).
https://www.nature.com/articles/s41467-019-11181-1
Depending on which results you are going to report, please also cite the original study of data sources/tools used in FUMA (references are available at links or tutorial for the cell type specificity analysis for scRNA-seq data).