mLeveraging genetic correlations to prioritize drug groups for repurposing in type 2 diabetes

Area of Science:

• Genetics
• Pharmacology
• Computational Biology

Background:

• Type 2 diabetes (T2D) is a complex polygenic disease with significant health implications.
• Genome-wide association studies (GWAS) have identified numerous T2D risk loci, but therapeutic translation is challenging.
• Limited success in translating genetic discoveries into effective T2D therapies necessitates novel approaches.

Purpose of the Study:

• To prioritize druggable gene sets for type 2 diabetes (T2D) by integrating genetic association data with drug-gene interaction information.
• To identify novel therapeutic targets and facilitate drug repurposing for T2D.
• To develop a genetically informed pipeline for identifying potential T2D treatments.

Main Methods:

• Applied a Bayesian Linear Regression (BLR) multi-trait gene set model to analyze GWAS summary statistics for T2D.
• Integrated genetic data with drug-gene interaction data from the Drug Gene Interaction Database (DGIdb).
• Calculated posterior inclusion probabilities (PIP) for drug groups (ATC 4th level) to assess genetic relevance.

Main Results:

• The model successfully validated known antidiabetic agents, demonstrating strong associations with T2D.
• Identified significant genetic relevance for carboxamide derivatives, fibrates, uric acid inhibitors, and immunomodulatory/antineoplastic agents.
• Highlighted key T2D-associated genes (e.g., PPARG, KCNQ1, TNF, GCK) and showed substantial genetic overlap for bezafibrate with T2D loci.

Conclusions:

• The study presents a novel, genetically informed pipeline for drug repurposing in T2D.
• Bezafibrate, a PPAR pan-agonist, shows potential for treating metabolic diseases due to its genetic overlap with T2D.
• This approach can accelerate the identification of new therapeutic strategies for type 2 diabetes.