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DrugRepurposing Pipeline

Summary

The DrugRepurposing pipeline is an open-source codebase developed by Solal Chauquet in the Shah Lab to implement a best-practice framework for robust, genetically-informed drug candidate prioritisation. The pipeline integrates genome-wide association study (GWAS) summary statistics with expression quantitative trait loci (eQTL) models using transcriptome-wide association studies (TWAS) to generate disease-associated gene expression signatures. It then compares these signatures against drug perturbation databases, such as the Connectivity Map (CMap), to identify potential therapeutic candidates based on their ability to reverse disease signatures.

Pipeline Architecture and Recommendations

The pipeline implements the benchmarked best practices described by Chauquet et al. (as of 2026-07-19): - TWAS Signature Generation: Supports gene expression prediction with sPrediXcan, FUSION, and sMultiXcan using GTEx reference models. - Signature Matching: Performs similarity calculations between the TWAS-derived disease signature and CMap drug signatures using Spearman correlation and Normalized Connectivity Score (NCS). The pipeline utilizes a locally re-processed version of CMap containing 221,549 unique drug perturbation consensus signatures. - Drug Class Enrichment: Utilizes the fgsea R package to perform Gene Set Enrichment Analysis (GSEA) on drug classes sharing a mechanism of action (MoA), providing a normalized enrichment score (NES) to evaluate drug prioritisation. - Best Practices Checklist: Recommends using biologically-relevant tissue-specific TWAS models (e.g., liver for LDL cholesterol), querying drug signatures in trait-relevant cell lines (e.g., HEPG2 cells for lipids), matching with Spearman correlation, and testing multiple small/unbalanced gene set sizes (from 5 to 60 genes per tail) to optimize signal-to-noise ratio.

Citations

  • Chauquet, S., Jiang, J. C., Barker, L. F., Hunter, Z. L., Singh, G., Wray, N. R., McRae, A. F., & Shah, S. (2026). From GWAS to drug: A framework for drug candidate prioritisation using a gene expression signature matching approach. medRxiv. Source paper: 2026.04.22.26349470v2.full.pdf