A Bayesian framework for longitudinal EHR and genetic discovery

Electronic health records (EHRs) provide rich longitudinal disease histories, but existing methods for analysing these data typically treat diseases in isolation¹ and rarely integrate germline genetics. Here we present ALADYNOULLI, a Bayesian generative framework that jointly models longitudinal EHR diagnoses, age and polygenic risk to recover latent time-varying disease signatures and patient-specific signature loadings; the model is formulated as a mixture of probabilities rather than a probability of a mixture², correctly accommodating simultaneous and chronic conditions. Applied to three independent biobanks (UK Biobank³, Mass General Brigham⁴ and All of Us; total n > 683,000) spanning up to 52 years of follow-up and 348 diseases, the model recovers 21 replicable signatures with high cross-cohort composition preservation (median of 80%) and reveals biological subtypes within diagnostic categories (Cohen's d up to 4.25; P ≤ 1 × 10^-8 for 95% of comparisons). Signatures are concordant with established disease biology: carriers of familial hypercholesterolaemia⁵ enrich in the cardiovascular signature; carriers of clonal haematopoiesis of indeterminate potential⁶ in the inflammation signature; and a rare variant burden in LDLR, TTN and BRCA2 (refs. ^7,8) aligns with disease specificities. A signature-based genome-wide association study identifies 151 genome-wide significant loci including cardiovascular associations missed by single-trait analyses. An explicit likelihood enables inverse probability weighting for selection bias⁹ while preserving biological signal. For disease prediction, ALADYNOULLI outperforms Pooled Cohort Equation (PCE), PREVENT and Gail at 1-year and 10-year horizons; disease-level (PheCode) predictions complement code-level foundation models such as Delphi-2M (ref. ¹⁰).