Connecting genetics and gene expression data for target prioritisation and drug repositioning

Developing new drugs continues to be a highly inefficient and costly business. By repurposing an existing compound for a different indication, drug repositioning offers an attractive alternative to traditional drug discovery. Most of these approaches work by matching transcriptional disease signatures to anti-correlated gene expression profiles of drug perturbations. Genome-wide association studies (GWASs) are of great interest to researchers in the pharmaceutical industry because drug programmes with supporting genetic evidence are more likely to successfully progress through the drug discovery pipeline. Here, we present a systematic approach to generate drug repositioning hypothesis based on disease genetics by mining public repositories of GWAS data and drug transcriptomic profiles. We find that genes genetically associated with a certain disease are more likely to be differentially expressed in the same disease (p-value = 1.54e-17 and AUC = 0.75) and that, in existing drug - disease combinations, genes significantly up-or down-regulated after drug treatment are enriched for genes genetically associated with that disease (p-value = 1.1e-79 and AUC = 0.64). Finally, we use this framework to generate and rank novel GWAS-driven drug repositioning predictions.