Abstract 4140333: Maximizing the yield of human genetics-based target discovery from circulating proteins for cardiometabolic diseases

Background: Circulating proteins are attractive candidate drug targets for cardiometabolic diseases. Identifying promising circulating proteins that have a causal role in disease pathogenesis is challenging and expensive. Mendelian randomization (MR) has emerged as an important tool for testing potential causal effects while mitigating biases from confounding and reverse causation. MR findings require validation by colocalization analysis to guard against bias due to correlation between genetic variants (linkage disequilibrium, LD). However, existing colocalization methods often fail to validate MR findings because of highly complex LD structures in the genome, rendering a low yield of human genetics-based target discovery. Methods: In this study, we developed a new computational method, called SharePro, to effectively assess colocalization evidence for MR-identified circulating proteins as targets for cardiometabolic diseases. Based on large-scale genome-wide association studies, we performed MR analyses to assess the associations between 1,535 circulating proteins and key cardiometabolic traits, including diastolic blood pressure, systolic blood pressure, serum hemoglobin A1c, serum low-density lipoprotein cholesterol, and serum triglycerides. We then benchmarked SharePro against state-of-the-art colocalization methods, including coloc, coloc+SuSiE, and PWCoCo, and evaluated the power and robustness of these methods in supporting MR findings. We further examined whether colocalization evidence-supported associations implicated known drug targets for cardiometabolic diseases. Results: SharePro demonstrated the highest power and robustness in supporting 160 (79.6%) of the 201 Bonferroni-significant protein-trait associations identified by MR, while existing methods supported up to 46.8% of these associations. Protein-trait associations identified by MR and supported by SharePro were more likely to implicate known drug targets for cardiometabolic diseases (Figure 1). Furthermore, eight protein-trait associations were exclusively supported by SharePro, suggesting novel targets, such as HSF1 and HAVCR2. Conclusions: SharePro most effectively supports promising protein-trait associations identified through MR for cardiometabolic diseases. Combining multiple lines of evidence using different methods may substantially increase the yield of human genetics-based drug target discovery by nearly twofold.