The majority of GWAS-associated variants fall in the non-coding part of the genome suggesting that they affect complex traits and diseases through altering expression of neighbouring genes using regulatory mechanisms. Identifying the causal gene underlying each association signal is an intense process requiring the integration of data from GWAS with transcriptomics, proteomics and epigenomics datasets from a wide range of cell types and tissues. Experimental validation is then used to confirm the likely causal variant(s) and the gene it is regulating. International efforts and consortia have used integrative approaches to identify target genes for specific therapy areas but there are currently no resources that leverage publicly available GWAS and functional genomics data in a scalable and reproducible way to systematically assign causal variants and target genes at all disease-associated loci.
