The Open Targets Platform (https://platform.opentargets.org/) is an open source resource to systematically assist drug target identification and prioritisation using publicly available data. Since our last update, we have reimagined, redesigned, and rebuilt the Platform in order to streamline data integration and harmonisation, expand the ways in which users can explore the data, and improve the user experience. The gene–disease causal evidence has been enhanced and expanded to better capture disease causality across rare, common, and somatic diseases. For target and drug annotations, we have incorporated new features that help assess target safety and tractability, including genetic constraint, PROTACtability assessments, and AlphaFold structure predictions. We have also introduced new machine learning applications for knowledge extraction from the published literature, clinical trial information, and drug labels. The new technologies and frameworks introduced since the last update will ease the introduction of new features and the creation of separate instances of the Platform adapted to user requirements. Our new Community forum, expanded training materials, and outreach programme support our users in a range of use cases.

No abstract

Supplementary data are available at Bioinformatics online.

Proteome-wide Mendelian randomization (MR) has emerged as a promising approach in uncovering novel therapeutic targets. However, genetic colocalization analysis has revealed that a third of MR associations lacked a shared causal signal between the protein and disease outcome, raising questions about the effectiveness of this approach. The impact of proteome-wide MR, stratified by cis-trans status, in the presence or absence of genetic colocalization, on therapeutic target identification remains largely unknown. In this study, we conducted genome-wide MR and cis/trans-genetic colocalization analyses using proteomic and complex trait genome-wide association studies. Using two different gold-standard datasets, we found that the enrichment of target-disease pairs supported by MR increased with more p-value stringent thresholds MR p-value, with the evidence of enrichment limited to colocalizing cis-MR associations. Using a phenome-wide proteogenetic colocalization approach, we identified 235 unique targets associated with 168 binary traits at high confidence (at colocalization posterior probability of shared signal > 0.8 and 5% FDR-corrected MR p-value). The majority of the target-trait pairs did not overlap with existing drug targets, highlighting opportunities to investigate novel therapeutic hypotheses. 42% of these non-overlapping target-trait pairs were supported by GWAS, interacting protein partners, animal models, and Mendelian disease evidence. Additionally, as previously reported, these high confidence target-trait pairs assisted with causal gene identification and helped uncover translationally informative novel biology, especially from trans-colocalizing signals, such as the association of lower intestinal alkaline phosphatase with a higher risk of inflammatory bowel disease in FUT2 non-secretors. Beyond target identification, we used MR of colocalizing signals to infer therapeutic directions and flag potential safety concerns. For example, we found that most genetically predicted therapeutic targets for inflammatory bowel disease could potentially worsen allergic disease phenotypes, except for TNFRSF6B where we observed directionally consistent associations for both phenotypes. Our results are publicly available (https://mk31.shinyapps.io/mr_app2/), enabling others to use proteogenomic evidence to appraise therapeutic targets.

Proteome-wide Mendelian randomization (MR) has emerged as a promising approach in uncovering novel therapeutic targets. However, genetic colocalization analysis has revealed that a third of MR associations lacked a shared causal signal between the protein and disease outcome, raising questions about the effectiveness of this approach. The impact of proteome-wide MR, stratified by cis-trans status, in the presence or absence of genetic colocalization, on therapeutic target identification remains largely unknown. In this study, we conducted genome-wide MR and cis/trans-genetic colocalization analyses using proteomic and complex trait genome-wide association studies. Using two different gold-standard datasets, we found that the enrichment of target-disease pairs supported by MR increased with more p-value stringent thresholds MR p-value, with the evidence of enrichment limited to colocalizing cis-MR associations. Using a phenome-wide proteogenetic colocalization approach, we identified 235 unique targets associated with 168 binary traits at high confidence (at colocalization posterior probability of shared signal > 0.8 and 5% FDR-corrected MR p-value). The majority of the target-trait pairs did not overlap with existing drug targets, highlighting opportunities to investigate novel therapeutic hypotheses. 42% of these non-overlapping target-trait pairs were supported by GWAS, interacting protein partners, animal models, and Mendelian disease evidence. These high confidence target-trait pairs assisted with causal gene identification and helped uncover translationally informative novel biology, especially from trans-colocalizing signals, such as the association of lower intestinal alkaline phosphatase with a higher risk of inflammatory bowel disease in FUT2 non-secretors. Beyond target identification, we used MR of colocalizing signals to infer therapeutic directions and flag potential safety concerns. For example, we found that most genetically predicted therapeutic targets for inflammatory bowel disease could potentially worsen allergic disease phenotypes, except for TNFRSF6B where we observed directionally consistent associations for both phenotypes. Our results are publicly available (https://mk31.shinyapps.io/mr_app2/), enabling others to use proteogenomic evidence to appraise therapeutic targets.

No abstract