Benchmarking Bayesian colocalization methods in validating Mendelian randomization-based target discoveries from circulating proteins for cardiometabolic diseases

BackgroundMendelian randomization (MR) is an important tool for identifying potential biomarkers and drug targets. Colocalization analysis is crucial for validating MR findings and guarding against potential confounding due to linkage disequilibrium. We aim to systematically benchmark the performance of four Bayesian colocalization methods in validating MR-based target discoveries from circulating proteins for cardiometabolic diseases. ResultsWe conducted MR analyses to assess the associations between circulating levels of 1,535 proteins and five cardiometabolic traits, followed by colocalization analyses using coloc, coloc+SuSiE, PWCoCo and SharePro. All methods demonstrated well-controlled false discoveries in the colocalization analysis of 611 pairs of circulating proteins and cardiometabolic traits with a nominal p-value > 0.9 in MR. SharePro demonstrated the highest frequency in supporting 160 (79.6%) of the 201 Bonferroni-significant protein-trait associations identified by MR, compared to coloc (supporting 40.3% of these associations), coloc+SuSiE (46.8%), and PWCoCo (45.8%), and was robust to varying prior colocalization probabilities. Moreover, protein-trait associations identified by MR and supported by SharePro were more likely to agree with significant gene-level associations based on rare variants detected in exome-wide association studies and implicate known drug targets for cardiometabolic diseases. Eight protein-trait associations were exclusively supported by SharePro and did not demonstrate a high risk of horizontal pleiotropy, suggesting potential cardiometabolic biomarkers or drug targets, such as HSF1 and HAVCR2. ConclusionsSharePro most often supports high-confidence associations identified through MR for cardiometabolic diseases. Combining multiple lines of evidence using different methods may substantially increase the yield of biomarker and drug target discovery programs.