G2DR: A Genotype-First Framework for Genetics-Informed Target Prioritization and Drug Repurposing

2026-03-20

AI Summary

Existing approaches struggle to effectively translate genotype signals into rankable drug targets and repurposing hypotheses in the absence of disease-matched transcriptomic data. This study proposes the first reproducible, genotype-initiated framework for target prioritization and drug repositioning that operates without reliance on disease-specific transcriptomes. The method integrates eQTL predictions, multi-source transcriptomic weights, pathway enrichment, network context, druggability assessments, and multidimensional drug–target evidence from Open Targets, DGIdb, and ChEMBL, augmented by a directionality filter to identify mechanistically compatible candidate drugs. Applied to migraine, the framework achieved gene-level ROC-AUC of 0.775 and PR-AUC of 0.475, successfully enriched known disease-relevant pathways, and uncovered promising candidates for drug repurposing.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Human genetics offers a promising route to therapeutic discovery, yet practical frameworks translating genotype-derived signal into ranked target and drug hypotheses remain limited, particularly when matched disease transcriptomics are unavailable. Here we present G2DR, a genotype-first prioritization framework propagating inherited variation through genetically predicted expression, multi-method gene-level testing, pathway enrichment, network context, druggability, and multi-source drug--target evidence integration. In a migraine case study with 733 UK Biobank participants under stratified five-fold cross-validation, we imputed expression across seven transcriptome-weight resources and ranked genes using a reproducibility-aware discovery score from training and validation data, followed by a balanced integrated score for target selection. Discovery-based prioritization generalized to held-out data, achieving gene-level ROC-AUC of 0.775 and PR-AUC of 0.475, while retaining enrichment for curated migraine biology. Mapping prioritized genes to compounds via Open Targets, DGIdb, and ChEMBL yielded drug sets enriched for migraine-linked compounds relative to a global background, though recovery favoured broader mechanism-linked and off-label space over migraine-specific approved therapies. Directionality filtering separated broadly recovered compounds from mechanistically compatible candidates. G2DR is a modular framework for genetics-informed hypothesis generation, not a clinically actionable recommendation system. All outputs require independent experimental, pharmacological, and clinical validation.

Problem

• genotype-first
• target prioritization
• drug repurposing
• human genetics
• transcriptomics

Innovation

• genotype-first
• drug repurposing
• target prioritization
• genetically predicted expression
• multi-source integration