Therapeutic target prediction for orphan diseases integrating genome-wide and transcriptome-wide association studies

Therapeutic target identification is challenging in drug discovery, particularly for rare and orphan diseases. Here, we propose a disease signature, TRESOR, which characterizes the functional mechanisms of each disease through genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) data, and develop machine learning methods for predicting inhibitory and activatory therapeutic targets for various diseases from target perturbation signatures (i.e., gene knockdown and overexpression). TRESOR enables highly accurate identification of target candidate proteins that counteract disease-specific transcriptome patterns, and the Bayesian optimization with omics-based disease similarities achieves the performance enhancement for diseases with few or no known targets. We make comprehensive predictions for 284 diseases with 4345 inhibitory target candidates and 151 diseases with 4040 activatory target candidates, and elaborate the promising targets using several independent cohorts. The methods are expected to be useful for understanding disease–disease relationships and identifying therapeutic targets for rare and orphan diseases. Identifying therapeutic targets is challenging, especially for orphan diseases. Here, the authors integrate GWAS and TWAS with machine learning methods to predict therapeutic targets for various diseases and demonstrate the usefulness in practice.