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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Published on: December 7, 2021

A Bayesian framework for longitudinal EHR and genetic discovery.

Sarah M Urbut1,2,3,4, Yi Ding5, Tetsushi Nakao6,7,8

  • 1Division of Cardiology, Heart and Vascular Institute, Massachusetts General Hospital, Boston, MA, USA. surbut@mgh.harvard.edu.

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|July 15, 2026
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Summary

We developed ALADYNOULLI, a new framework using electronic health records and genetics to discover disease patterns. This approach identifies biological subtypes and improves disease prediction, outperforming existing models.

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Area of Science:

  • Computational biology
  • Genetics
  • Epidemiology

Background:

  • Electronic health records (EHRs) offer rich longitudinal disease data but often analyze diseases in isolation, neglecting germline genetics.
  • Existing analytical methods struggle to model simultaneous and chronic conditions effectively.

Purpose of the Study:

  • To introduce ALADYNOULLI, a Bayesian generative framework for jointly modeling longitudinal EHR diagnoses, age, and polygenic risk.
  • To uncover latent, time-varying disease signatures and patient-specific loadings by accommodating complex disease states.

Main Methods:

  • Developed a Bayesian generative framework (ALADYNOULLI) formulated as a mixture of probabilities.
  • Applied the model to three large, independent biobanks (UK Biobank, Mass General Brigham, All of Us) with extensive follow-up data.
  • Integrated longitudinal EHR diagnoses, age, and polygenic risk scores.

Main Results:

  • Recovered 21 replicable disease signatures with high cross-cohort preservation (median 80%).
  • Identified distinct biological subtypes within diagnostic categories, showing significant differences (Cohen's d up to 4.25).
  • Signature-based GWAS identified 151 significant loci, including novel cardiovascular associations; ALADYNOULLI improved disease prediction accuracy over established models.

Conclusions:

  • ALADYNOULLI effectively models complex disease trajectories by integrating EHRs and genetics.
  • The framework reveals biologically meaningful disease subtypes and enhances predictive capabilities.
  • This approach offers a powerful tool for understanding disease heterogeneity and improving patient risk stratification.