We fit multivariate logistic regression models predicting target-indication pair approval using several independent variables. The first was a measure of (continuous) genetic evidence, defined as the maximum semantic similarity to the indication across all traits linked to the drug target through human genetic evidence. The remaining independent variables are target and indication-level properties that could confound the relationship between genetic evidence and approval. Previous work has shown that approved drug targets tend to be more conserved than genes linked to GWAS associations [16], so we included residual variant intolerance score (RVIS) [17], measuring the amount of common functional variation in each gene relative to the amount of neutral variation, as a predictor. We also included the amount of time each target is known to have been under development as a predictor, with the rationale that if accumulating genetic evidence informs drug development, targets supported by genetic evidence might be newer on average. Finally, we included gene ontology (GO) terms and high level MeSH terms for each indication as predictors to control for known differences [18, 19] in approval probability among indication and target classes.
