Abstract
Motivation Unintended effects of medications on diverse diseases are often identified many years after these drugs enter common use. This may be because drugs can have effects on multiple molecular targets, influencing unexpected biological processes. Discovering how biological effects of drugs relate to disease biology can provide insight into the basis for these latent drug effects, and help predict new effects. Rich data now comprehensively profile both the biological processes impacted by common drugs, and the human phenotypes known to be affected by these drugs. At the same time, systematic phenome-wide genetic studies associate each common phenotype with its genetic drivers. Here, we develop a method to integrate this data to learn how drug molecular effects can explain drug effects on the phenome.
Results We develop a supervised approach to quantify how a drug’s effect on phenotype can be explained by learned connections between the drug’s molecular effects and the genetic drivers of phenotypes. Our predictions of drug phenotype relationships outperform a baseline model. But more importantly, by projecting each drug to the space of its influence on phenotypes, we present evidence that our learned interaction matrix captures information about drug biology. We use the results to propose biological mechanisms by which drugs that share a target influence disease biology.
Availability Code to reproduce the analysis is available at https://github.com/RDMelamed/drug-phenome Predicted phenotypic effects for each drug and drug disease genomics matrix are available at https://figshare.com/projects/Integrating disease genetics and drug bioassays to discover drug impacts on the human phenome/157731
Competing Interest Statement
The authors have declared no competing interest.
