We have been comparing Open Targets with newer genetics-focused resources such as GPMap. While Open Targets excels at target prioritization by integrating genetics, biology, and therapeutic evidence, GPMap provides extensive colocalization and molecular QTL evidence that can help improve causal gene assignment at disease-associated loci.
In our experience, one of the key challenges in human genetics–driven drug discovery is moving from a GWAS signal to a high-confidence causal target. Resources that systematically integrate eQTL, pQTL, and other molecular QTL data through colocalization analyses can provide valuable mechanistic insights and help distinguish between nearby candidate genes. This is particularly important for RNA-targeted therapeutics, where selecting the correct target is often the critical decision point.
Has the Open Targets team considered incorporating large-scale colocalization-derived confidence metrics as an additional evidence layer for target–disease associations? It would be interesting to understand how such evidence might complement existing genetics-based approaches, including fine-mapping and locus-to-gene methodologies.
What do you think? I’d love to learn how others in the community are combining Open Targets with resources such as GPMap, colocalization analyses, and molecular QTL data in their target discovery workflows, and what approaches have been most effective in practice. Looking forward to hearing different perspectives and learning from the community.