Demystifying non-coding GWAS variants: an overview of computational tools and methods

Abstract

Genome-wide association studies (GWAS) have found the majority of disease-associated variants to be non-coding. Major efforts into the charting of the non-coding regulatory landscapes have allowed for the development of tools and methods which aim to aid in the identification of causal variants and their mechanism of action. In this review, we give an overview of current tools and methods for the analysis of non-coding GWAS variants in disease. We provide a workflow that allows for the accumulation of in silico evidence to generate novel hypotheses on mechanisms underlying disease and prioritize targets for follow-up study using non-coding GWAS variants. Lastly, we discuss the need for comprehensive benchmarks and novel tools for the analysis of non-coding variants.

Figure 1

Workflow from non-coding variants to disease insights. Relevant annotation and visualization tools are highlighted in Table 1. Pathogenicity prediction tools are listed in Table 2. Tools and methods for MolQTL analysis are listed in Table 3. Finally, tools and methods for functional mapping and integration of experimental SNP-to-gene data can be found in Table 4.