Muhammad Muneeb, David B Ascher
Human genetics offers a scalable route to therapeutic discovery, but practical frameworks that convert genotype-derived signal into ranked target and drug hypotheses remain limited, particularly when matched disease transcriptomics are unavailable. We present G2DR, a genotype-first computational prioritization framework that integrates genetically predicted gene expression, multi-method gene-level testing, pathway enrichment, network context, druggability, and multi-source drug-target evidence to generate hypotheses for downstream follow-up. In a migraine case study of 733 UK Biobank participants (53 cases, 680 controls) using stratified five-fold cross-validation, G2DR imputed genetically regulated expression across seven transcriptome-weight resources and ranked genes using a reproducibility-aware discovery score derived only from training and validation data, followed by a balanced integrated score for target selection. Internal held-out evaluation within the same UK Biobank-derived analytical framework achieved gene-level ROC-AUC of 0.775 and PR-AUC of 0.475 for recovery of test-significant genes, while retaining enrichment for curated migraine-associated biology. Mapping prioritized genes to compounds through Open Targets, DGIdb, and ChEMBL produced drug sets enriched for migraine-linked and literature-associated compounds relative to a global drug background. However, tiered benchmarking showed limited recovery of migraine-specific approved therapies, with stronger signal from mechanism-linked, off-label, and literature-associated pharmacological space. Directionality filtering further distinguished broadly recovered compounds from those with stronger mechanistic compatibility. G2DR is, therefore, best viewed as a modular framework for genetics-informed hypothesis generation in genotype-first settings, not as a clinically actionable target-identification or drug-recommendation system. Prioritized genes and compounds require independent validation.