Functional mapping and annotation of genetic associations with FUMA - PubMed (original) (raw)

Functional mapping and annotation of genetic associations with FUMA

Kyoko Watanabe et al. Nat Commun. 2017.

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing financial interests.

Figures

Fig. 1

Fig. 1

Overview of FUMA. FUMA includes two core processes, SNP2GENE and GENE2FUNC. The input is GWAS summary statistics. SNP2GENE prioritizes functional SNPs and genes, outputs tables (blue boxes), and creates Manhattan, quantile–quantile (QQ) and interactive regional plots (box at right bottom). GENE2FUNC provides four outputs; a gene expression heatmap, enrichment of differentially expressed gene (DEG) sets in a certain tissue compared to all other tissue types, overrepresentation of gene sets, and links to external biological information of input genes. All results are downloadable as text files or high-resolution images

Fig. 2

Fig. 2

Overview of prioritized genes from BMI GWAS by FUMA. Starting from the BMI GWAS summary statistics, boxes represent results of the SNP2GENE process. The annotated SNPs include all independent lead SNPs and SNPs which are in LD with these lead SNPs. Prioritized genes are divided into three categories; genes that are implicated by deleterious coding SNPs (colored pink), by eQTLs for these genes (colored blue), or by chromatin interactions (colored green). The prioritized genes are further categorized into previously reported genes (blue) and novel genes (red) prioritized genes by FUMA. *These genes were not prioritized by FUMA since they do not have either deleterious coding SNPs, eQTLs or chromatin interactions, although they are located within GWAS risk loci

Fig. 3

Fig. 3

Regional plot of the locus 16q.12.2 of BMI GWAS. a Extended region of the FTO locus, which includes prioritized genes RBL2 and IRX3. Genes prioritized by FUMA are highlighted in red. b Zoomed in regional plot of FTO locus with, from the top, GWAS _P_-value (SNPs are colored based on r 2), CADD score, RequlomeDB score and eQTL _P_-value. Non-GWAS-tagged SNPs are shown at the top of the plot as rectangles since they do not have a _P_-value from the GWAS, but they are in LD with the lead SNP. eQTLs are plotted per gene and colored based on tissue types. In the plots of CADD score, RegulomeDB score and eQTLs, SNPs which are not mapped to any gene are colored gray

Fig. 4

Fig. 4

Chromatin interactions and eQTLs of BMI risk loci on chr. 16. The most outer layer is the Manhattan plot displaying SNPs with _P_-value < 0.05. Candidate SNPs are colored based on the highest _r_ 2 to one of the independent significant loci (red: _r_ 2 > 0.8, orange: r 2 > 0.6). Other SNPs are colored in gray. rsID of top SNPs per locus are labeled. The outer circle is the chromosome coordinate and genomic risk loci are highlighted in blue. Genes mapped by either Hi-C or eQTLs are shown on the inner circle. Genes mapped by Hi-C, eQTLs are colored orange and green, respectively. Genes mapped by both are colored red. Chromatin interaction and eQTLs are shown as links colored orange and green respectively

References

    1. Welter D, et al. The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–D1006. doi: 10.1093/nar/gkt1229. - DOI - PMC - PubMed
    1. Wood AR, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 2014;46:1173–1186. doi: 10.1038/ng.3097. - DOI - PMC - PubMed
    1. Ripke S, et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–427. doi: 10.1038/nature13595. - DOI - PMC - PubMed
    1. Okbay A, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533:539–542. doi: 10.1038/nature17671. - DOI - PMC - PubMed
    1. Sudlow C, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:1–10. doi: 10.1371/journal.pmed.1001779. - DOI - PMC - PubMed

Publication types

MeSH terms

Substances

LinkOut - more resources