17 - FUMA: FUNCTIONAL MAPPING AND ANNOTATION OF GENETIC ASSOCIATIONS

Abstract

Background A main challenge in genome-wide association studies (GWAS) is to prioritize genetic variants and identify potential causal mechanisms of human diseases. Although a variety of bioinformatics tools are now available for downstream analyses of GWAS results, a standard, integrative approach is lacking. We developed FUMA: a web-based platform to facilitate functional annotation of GWAS results, prioritization of potential causal genetic variants and genes, and interactive visualization by incorporating 14 biological data repositories and tools. FUMA is available as an online tool at http://fuma.ctglab.nl. Methods FUMA contains two core functions to annotate input summary statistics to prioritize potential causal genetic variants and genes; SNP2GENE and GENE2FUNC. In SNP2GENE, SNPs are annotated with their biological functionality and mapped to genes based on positional and functional information of SNPs. In this process, significantly associated SNPs from GWAS are characterized as genomic risk loci by incorporating the linkage disequilibrium structure. Functionally annotated SNPs are mapped to genes based on functional consequences on genes (positional mapping), expression quantitative trait loci (eQTLs) and chromatin interactions of phenotype relevant tissue types (eQTL and chromatin interaction mappings). By combining these three mapping strategies, FUMA enables to prioritize genes that are highly likely involved in the trait of interest. To obtain insight into putative causal mechanisms, the GENE2FUNC process annotates the prioritized genes in biological context, such as tissue specific gene expression pattern, enrichment of gene sets and direct links to well defined external biological databases such as disease associated genes and drug targets. Results We have applied FUMA to the most recent Body Mass Index (BMI)[1]. We successfully prioritized previously reported genes from the original GWAS study, and also novel candidates by combining positional mapping of deleterious coding SNPs and eQTL mapping. For example, IRX3 was prioritized by eQTLs from FTO locus which is the most significantly associated locus with BMI. Although, FTO is the only gene reported in the original study since the genomic risk locus is located within the FTO gene and not overlapped with any other genes, IRX3 has been recently validated as whose expression is affected by variants in the FTO locus[2]. Thus, by incorporating multiple biological information, we could prioritise genes that are located outside of genomic risk loci. Chromatin interaction mapping identified additional novel candidates, including genes located outside of the risk loci, which showed share biological functions with the previously reported genes. Discussion In summary, FUMA provides an easy-to-use tool to interpret and functionally annotate results from genetic association studies. It allows to quickly gain insight into the directional biological implications of significant genetic associations. FUMA combines information of state-of-the-art biological data sources in a single platform to facilitate the generation of hypotheses for functional follow-up analysis aimed at proving causal relations between genetic variants and diseases.