Genetic dose-response modelling predicts drug mechanisms, dosing, and adverse events

Understanding how changes in gene function affect disease risk is central to drug development. Genetic variants are natural perturbations of gene activity and provide an opportunity to systematically model dose-response relationships between genes and phenotypes. Here, we present Variant-Informed Dose-Response Analysis (VIDRA), a computational framework that integrates trait-associated variants spanning a spectrum of allele frequencies and functional consequences within a hierarchical Bayesian regression model to systematically infer genetic dose-response relationships. Using 1,607,115 phenotypically associated germline variants available through Open Targets data (including common trait, rare disease, and gene burden associations), we systematically modelled how genetically driven alterations in gene function relate to disease risk, generating 148,350 dose-response-like gene-phenotype relationships across 7,681 phenotypes. Incorporating rare variant information alongside common variants resulted in a 7.23-fold increase in unique phenotypes, and a 51% increase in gene-disease pair associations. We calibrated our model against known drug targets to derive VIDRA Therapeutic Potential Score to rank genes based on their likelihood of succeeding as therapies, and identified 1,860 significant genes, 87% of which are currently therapeutically not targeted. VIDRA framework captures the direction and magnitude of gene-phenotype dependencies, enabling insights beyond target identification. Sixty-two percent of high-scoring targets are predicted to benefit from agonistic modulation, highlighting untapped potential for therapeutic activation. Furthermore, VIDRA framework extends to modelling relationships between genes, intermediate phenotypes, and diseases, enabling identification of biomarker-disease correlations. Applied to blood-cell and cardiovascular traits, VIDRA recovered known genetic links, suggesting a capacity to identify novel biomarker-disease relationships. Lastly, we observed a positive correlation by comparing VIDRA slopes with drug dose-response data from clinical trials, supporting the use of genetic data as a proxy for pharmacological titration. Together, VIDRA framework provides a generalizable, interpretable approach to inform multiple stages of drug development, from target prioritization and therapeutic direction of modulation prediction to biomarker identification, dose guidance, and safety risk assessment.