Functional mapping and annotation of genetic associations with FUMA

Abstract

A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.

Genome-wide association analysis of diverticular disease points towards neuromuscular, connective tissue and epithelial pathomechanisms

“…We performed additional hypothesis-free functional and gene annotations based on the genomic positions of risk loci using FUMA17 as the curated candidate genes might not capture the full biology of the risk architecture. Positional gene mapping aligned SNPs to 176 genes, eQTL gene mapping matched cis -eQTL SNPs to 269 genes whose expression levels they influence (snp-gene pairs with FDR<0.05), with 21 genes specifically affected in sigmoid colon (online supplementary table 20).…”

“…Because the majority of lead variants were located in non-coding regions and thus not directly amendable to functional interpretation, we used GARFIELD to analyse enrichment statistics for the diverticular disease GWAS risk dataset with cell-specific coding, non-coding and functional elements from the GENCODE, ENCODE and Roadmap projects 17. A graphical summary of the enrichment of DNAse I hypersensitive sites is provided in online supplementary figure 11.…”

“…Genomic risk loci, lead variants and candidate single nucleotide polymorphism (SNPs) were derived from FUnctional Mapping and Annotation of genetic associations (FUMA V.1.3.1)17 based on GWAS summary statistics. Candidate SNP and gene positions are provided in online supplementary table 1 and 2.…”

“…In order to identify candidate gene(s) at the respective genomic risk locus, we followed i) a manually curated selection process based on local linkage disequilibrium (LD) structure and supporting evidence from regulatory elements (eQTL and chromatin interaction), outlined in online supplementary table 3 and ii) we performed hypothesis-free functional and gene annotations based on the genomic positions of risk loci using FUMA,17 as the manually curated selection process of candidate genes might not capture the full biology of the risk architecture, as detailed in online supplementary materials and methods section.…”

Genome‐wide association analysis links multiple psychiatric liability genes to oscillatory brain activity

“…This was performed by several means. SNP-based GWAS results were followed up with gene-based analyses and expression-based enrichment analyses, which further increases power to detect genes affecting functional brain activity [Neale and Sham, 2004; Ripke et al, 2014; Watanabe et al, 2017]. In addition, significant results from gene-based tests were compared to known liability genes for behavioral phenotypes including neuropsychiatric disorders by searching GWAS results databases (ensembl.org, gwascentral.org).…”