PheWAS

MixEHR-SAGE is a PheCode-guided multi-modal topic model that integrates diagnoses, procedures, and medications from EHR to enhance phenotyping for GWAS. By combining expert-informed priors with probabilistic inference, it identifies over 1000 interpretable phenotype topics from UK Biobank data and improves disease incidence prediction and GWAS discovery. Published in Briefings in Bioinformatics.

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. Applied to 350,000 individuals with high-quality genetic data, MixEHR-SAGE-derived risk scores accurately predicted disease incidence and improved GWAS discovery.

FinnGen R7 data freeze of 224,737 participants analyzed across 1,932 disease endpoints. Identified 30 new low-frequency variant associations enriched in Finland, and 2,733 genome-wide significant associations through phenome-wide scanning. Published in Nature (Kurki et al., 2023).

Expanded disease-specific GWAS across hundreds of traits leveraging the deep EHR phenotyping in the VA system. Whole-genome sequencing of a subset of participants for comprehensive variant discovery. MVP data contributed to multi-biobank meta-analyses with FinnGen and UK Biobank spanning thousands of phenotypes.

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases, using pQTL data from UKB-PPP and other cohorts.