ALADYNOULLI: A Bayesian approach to disease progression modeling for genomic discovery and clinical prediction.

Understanding how disease patterns evolve over a lifetime remains a key challenge in medicine. While electronic health records provide rich longitudinal data, existing models typically analyze each disease in isolation, missing the complex interplay between multiple conditions and genetic factors. Here, we combine longitudinal health records with genetic data to model individual trajectories, using a novel dynamic Bayesian framework called ALADYNOULLI that identifies latent disease signatures from longitudinal health records while modeling individual-specific trajectories. Applied across three biobanks with up to 50 years of follow-up, our model discovers clinically interpretable disease signatures that demonstrate remarkable consistency across diverse populations (79.2% cross-cohort correspondence) and show strong genetic correlations, enabling both accurate prediction of patient risk and discovery of novel genetic associations. The model achieves dramatic improvements in disease prediction across 24 conditions, outperforming established clinical risk scores like PCE, PREVENT and GAIL over short and longer-term horizons. Furthermore, our signature-based approach identifies over 150 genetic loci - many missed by single-disease GWAS - with multiple signatures showing strong genetic signals (cardiovascular h