GNLGMar 20

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

arXiv:2603.203461.0h-index: 1
Predicted impact top 99% in GN · last 90 daysOriginality Incremental advance
AI Analysis

This work addresses the challenge of genetics-informed therapeutic discovery for researchers and drug developers, though it is incremental as it builds on existing prioritization methods with a modular framework.

The researchers tackled the problem of translating genetic data into prioritized drug targets and repurposing candidates, particularly when disease-specific transcriptomics are lacking, by developing the G2DR framework, which achieved a gene-level ROC-AUC of 0.775 and PR-AUC of 0.475 in a migraine case study with 733 UK Biobank participants. The framework integrated multiple data sources to rank genes and map them to compounds, showing enrichment for migraine-linked biology and broader mechanism-linked compounds.

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.

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