| Title: | PheCode-guided multi-modal topic modeling of electronic health records improves disease incidence prediction and GWAS discovery from UK Biobank |
| Journal: | Briefings in Bioinformatics |
| Published: | 7 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41627341/ |
| DOI: | https://doi.org/10.1093/bib/bbag030 |
Publication 18726
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Abstract
Phenome-wide association studies rely on disease definitions derived from diagnostic codes, often failing to leverage the full richness of electronic health records (EHR). We present MixEHR-SAGE, a PheCode-guided multi-modal topic model that integrates diagnoses, procedures, and medications to enhance phenotyping from large-scale EHRs. By combining expert-informed priors with probabilistic inference, MixEHR-SAGE identifies over 1000 interpretable phenotype topics from UK Biobank data. Applied to 350 000 individuals with high-quality genetic data, MixEHR-SAGE-derived risk scores accurately predict incident type 2 diabetes (T2D) and leukemia diagnoses. Subsequent genome-wide association studies using these continuous risk scores uncovered novel disease-associated loci, including PPP1R15A for T2D and JMJD6/SRSF2 for leukemia, that were missed by traditional binary case definitions. These results highlight the potential of probabilistic phenotyping from multi-modal EHRs to improve genetic discovery. The MixEHR-SAGE software is publicly available at: https://github.com/li-lab-mcgill/MixEHR-SAGE.</p>
11 Keywords
- Biological Specimen Banks
- Diabetes Mellitus, Type 2
- Electronic Health Records
- Genome-Wide Association Study
- Humans
- Incidence
- Leukemia
- Phenotype
- Software
- UK Biobank
- United Kingdom
5 Authors
- Ziqi Yang
- Ziyang Song
- Shadi Zabad
- Marc-André Legault
- Yue Li
1 Application
| Application ID | Title |
|---|---|
| 45551 | Mechanistic modelling of neurodevelopment, neuroplasticity, and neurodegeneration |
Enabling scientific discoveries that improve human health