G2DR: a genotype-first framework for genetics-informed target prioritization and drug repurposing

Area of Science:

• Genetics and Bioinformatics
• Computational Biology
• Pharmacology

Background:

• Human genetics provides a pathway for drug discovery, but practical tools for translating genetic data into ranked therapeutic targets and drug hypotheses are limited.
• Existing frameworks often struggle, particularly when matched disease transcriptomics data is unavailable.

Purpose of the Study:

• To present G2DR, a genotype-first computational framework for prioritizing therapeutic targets.
• To integrate genetically predicted gene expression, gene-level testing, pathway enrichment, network context, druggability, and drug-target evidence for hypothesis generation.

Main Methods:

• G2DR integrates genetically predicted gene expression from multiple resources.
• It employs a reproducibility-aware discovery score for gene ranking and a balanced integrated score for target selection.
• A migraine case study using UK Biobank data was performed with stratified cross-validation.

Main Results:

• Internal evaluation demonstrated strong gene-level performance (ROC-AUC 0.775, PR-AUC 0.475) for identifying significant genes.
• Prioritized genes showed enrichment for migraine-associated biology.
• Generated drug sets were enriched for migraine-linked and literature-associated compounds, though approved therapies were limited.

Conclusions:

• G2DR is a modular framework for genetics-informed hypothesis generation in genotype-first scenarios.
• It is not a clinical target identification or drug recommendation system; prioritized genes and compounds require independent validation.
• The framework shows promise for identifying novel therapeutic avenues by leveraging genetic insights.