Leveraging large-scale multi-omics to identify therapeutic targets from genome-wide association studies

BACKGROUND: Therapeutic targets supported by genetic evidence from genome-wide association studies (GWAS) show higher probability of success in clinical trials. GWAS is a powerful approach to identify links between genetic variants and phenotypic variation; however, identifying the genes driving associations identified in GWAS remains challenging. Integration of molecular quantitative trait loci (molQTL) such as expression QTL (eQTL) using mendelian randomization (MR) and colocalization analyses can help with the identification of causal genes. Careful interpretation remains warranted because eQTL can affect the expression of multiple genes within the same locus. METHODS: We used a combination of genomic features that include variant annotation, activity-by-contact maps, MR, and colocalization with molQTL to prioritize causal genes across 4,611 disease GWAS and meta-analyses from biobank studies, namely FinnGen, Estonian Biobank and UK Biobank. RESULTS: Genes identified using this approach are enriched for gold standard causal genes and capture known biological links between disease genetics and biology. In addition, we find that eQTLs colocalizing with GWAS are statistically enriched for corresponding disease-relevant tissues. We show that predicted directionality from MR is generally consistent with matched drug mechanism of actions (>78% for approved drugs). Compared to the nearest gene mapping method our approach also shows a higher enrichment in approved therapeutic targets (risk ratio 1.38 vs 2.06). Finally, using this approach, we detected a novel association between the IL6 receptor signal transduction gene IL6ST and polymyalgia rheumatica, an indication for which sarilumab, a monoclonal antibody against IL-6, has been recently approved. CONCLUSIONS: Combining variant annotation and activity-by-contact maps to molQTL increases performance to identify causal genes, while informing on directionality which can be translated to successful target identification and drug development.