Genetics-driven Risk Predictions with Differentiable Mendelian Randomization

Abstract Accurate predictive models of future disease onset are crucial for effective preventive healthcare, yet longitudinal datasets linking early risk factors to subsequent health outcomes are scarce. To address this challenge, we introduce Differentiable Mendelian Randomization (DMR), an extension of the classical Mendelian Randomization framework to learn risk predictors without longitudinal data. To do so, DMR leverages risk factors and genetic data from a healthy cohort, along with results from genome-wide association studies (GWAS) of diseases of interest. After training, the learned predictor can be used to assess risk for new patients solely based on risk factors. We validated DMR through comprehensive simulations and in future type 2 diabetes predictions in UK Biobank participants without diabetes, using follow-up onset labels for validation. Finally, we apply DMR to predict future Alzheimer’s onset from brain imaging biomarkers. Overall, with DMR we offer a new perspective in predictive modeling, showing it is possible to learn risk predictors leveraging genetics rather than longitudinal data. Competing Interest Statement The authors have declared no competing interest.