Due to the MeSH ontology structure, current methods require manual similarity assignments to recognize relationships between most quantitative traits and diseases. The high sensitivity of key results to MeSH similarity motivates treating similarity as a continuous variable and suggests improvements to its quantification. While expert curation can be advantageous in identifying closely related traits, it also leaves more room for human input to bias the analysis outcome. To assess this we removed automatically assigned similarities. Positive associations between GWAS genetic evidence and approval remain, though in some cases are greatly reduced in magnitude (S19 and S26 Figs) (OMIM is minimally impacted as it contains few quantitative traits). We expect improved methods automatically identifying similar phenotypes to drug indications will expand our ability to use genomics data in predictive models.
