Overview
The aim of Open Targets Genetics is to aggregate evidence linking (i) variants to disease, and (ii) variants to genes, so that for a specific disease potential drug targets can be prioritised based on robust genetic information.
Disease association information (
= study) is obtained from genome-wide association studies (GWAS) which link disease status (or other trait measurements) to common genetic variation. Due to how GWAS results are reported, we often only know the lead variant (
) at each associated locus. However, it cannot be assumed that the lead variant is causing the association, instead, we expand the lead variant to include all tag variants (
), which make up a more complete set of potentially causal variants. The lead to tag expansions are made using two methods: (i) fine-mapping / credible set analysis, where full summary statistics are available; (ii) linkage-disequilibrium expansion.
Given a set of potentially causal tag variants, we next assign these to genes (
) using our variant-to-gene (V2G) pipeline. The V2G pipeline combines data from four sources:
Molecular phenotype quantitative trait loci experiments (e.g. eQTLs and pQTLs)
Chromatin interaction experiments (e.g. Promoter Capture Hi-C)
In silico functional predictions (e.g. Variant Effect Predictor from Ensembl)
Distance from the canonical transcript start site (TSS)
For each variant, the pipeline first assigns functional evidence to variant-gene pairs (V, G) across all sources, then applies a scoring algorithm to produce aggregated V2G scores. Detailed methods can be found here.
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