Abstract
Genome Wide Association Studies (GWAS) provide an unbiased discovery mechanism for numerous human diseases. However, a frustration in the analysis of GWAS is that the majority of variants discovered do not directly alter protein-coding genes. We have developed a simple analysis approach that detects the tissue-specific regulatory component of a set of GWAS SNPs by identifying enrichment of overlap with DNase I hotspots from diverse tissue samples. Functional element Overlap analysis of the Results of GWAS Experiments (FORGE) is available as a web tool and as standalone software and provides tabular and graphical summaries of the enrichments. Conducting FORGE analysis on SNP sets for 260 phenotypes available from the GWAS catalogue reveals numerous overlap enrichments with tissue-specific components reflecting the known aetiology of the phenotypes as well as revealing other unforeseen tissue involvements that may lead to mechanistic insights for disease.
Highlights
A primary motivation for sequencing the human genome was to shed light on mechanisms involved in human disease
FORGE analysis takes a set of single nucleotide polymorphisms (SNPs), such as those SNPs reported above the genome wide significance threshold (p < 5e-8) in a Genome Wide Association Studies (GWAS) study, optionally filters the SNPs to remove all bar one SNP from a region in high LD (“LD pruning”) and determines whether there is enrichment for overlap with putative regulatory elements compared to a matched background of SNP sets
For each set of test SNPs, an overlap analysis is performed against the DNase I hotspots for each available cell sample separately (125 samples for ENCODE, 299 for Roadmap, described in Supplementary Table S1), and the number of overlaps is counted
Summary
A primary motivation for sequencing the human genome was to shed light on mechanisms involved in human disease. Notwithstanding that the reported variant for an association may be in linkage disequilibrium with a causal variant affecting a protein coding sequence, regulatory regions have been demonstrated to be linked to both specific diseases associations[6,7,8,9,10,11,12,13,14,15,16,17,18] (see 19 for review and further examples) and to be enriched in bulk in SNPs found across all GWAS2,20–22. To control for the processes involved in selecting SNPs for genotyping, only 1000 genomes phase 1 SNPs that had been included on one of the common genotyping platforms as described in Ensembl (http://www.ensembl.org/info/genome/variation/data_description.html#variation_sets) were considered further. For each SNP in a test set, the corresponding bin is identified based on its GC, maf and TSS distance, and background selections are made from that bin
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