To underscore the applicability of our tool, we tested a wide range of gene sets including large-scale GWASs, tissue-specific gene expression data released by GTEx Consortium ( GTEx Consortium et al., 2017), and a somatic mutation catalog of various cancers by COSMIC Consortium ( Forbes et al., 2017).įirst, as an illustrative example, we applied GREP to the risk genes identified by a recent multi-ancestry genome-wide association meta-analysis of stroke by MEGASTROKE consortium ( Malik et al., 2018). The benchmarking shows that the standard GREP analysis takes 1.71 s on MacBook Pro (2.4 GHz, Intel Core i7) using a single CPU. ![]() GREP is implemented as a python module and runs all the analysis in a few seconds on Linux or Mac OS environment. We note that our implementation to identify pharmacologically associated drug indication classes or anatomical classes from the flexible gene set is the novel feature when compared with the previous gene-drug-disease databases (as in Koscielny et al., 2017). Our method is novel in that (i) users can input a gene set of any origin, and (ii) the drugs in the database are organized by their clinical indications, so as to grasp the whole picture of the pharmacological enrichment. Further, GREP outputs the names of the drugs targeting the gene set, which are thus considered to have an association with the basis on which the gene set was selected. Using a text file of genes as a user input together with these pre-formatted databases, the GREP software automatically performs a series of Fisher’s exact tests to examine whether the gene set is enriched in genes targeted by drugs in a clinical indication category to treat a certain disease or condition. Each circle represents an enrichment P value of the drug indication category by ICD10 (c) Comparative enrichment analysis of oncogenes and tumor suppressor genes by GREP. MI, myocardial infarction PE, pulmonary embolism DVT, deep vein thrombosis. The inset shows the approved drugs targeting the gene set and their current indications. ![]() (b) Enrichment analysis of stroke-risk suggestive genes by GREP. TTD, Therapeutic Target Database ATC, Anatomical Therapeutic Chemical Classification ICD10, International Classification of Diseases 10. (a) Schematic presentation of GREP software. The overview of GREP and its application to GWAS and cancer somatic mutations. ![]() GREP also unraveled new insights into the biology of the tissue-specific gene expression and the genetic landscape of cancers. By applying GREP to a large-scale GWAS of stroke, a gene expression analysis on human tissues, and somatic mutations in cancers, we show that genes implicated in the GWAS were robustly enriched in the indicated medications for the trait of interest. Here we introduce a software GREP ( Genome for REPositioning drugs), which (i) quantifies an enrichment of the user-defined set of genes in the target of clinical indication categories and (ii) captures potentially repositionable drugs targeting the gene set. Repositioning known drugs to another indication is an effective way to bring them to bedside, because their efficacy and potential adverse effects have already been investigated for the current indication. Given the fact that more than half of clinical trials fail because of lack of efficacy or adverse events ( Nelson et al., 2015), the method to discover new therapeutics with the guidance of genomics should be warranted ( Malik et al., 2018 Okada et al., 2014). After the fruitful decades of genome-wide association studies (GWASs) identifying thousands of loci robustly associated with human complex traits ( Welter et al., 2014), we are now heading to the next step to apply in silico genetic knowledge for clinical practice.
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