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Shadi Zabad

Alumni

Publications

PheCode-guided multi-modal topic modeling of electronic health records improves disease incidence prediction and GWAS discovery from UK Biobank
Ziqi Yang
Ziyang Song
Shadi Zabad
Marc-André Legault
Yue Li
Phenome-wide association studies rely on disease definitions derived from diagnostic codes, often failing to leverage the full richness of e… (voir plus)lectronic 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.
2025-12-31
Briefings in Bioinformatics (publié)
doi.org
Toward whole-genome inference of polygenic scores with fast and memory-efficient algorithms.
Shadi Zabad
Chirayu Anant Haryan
Simon Gravel
Sanchit Misra
Yuemei Li
2025-07-02
American Journal of Human Genetics (publié)
doi.org
Towards whole-genome inference of polygenic scores with fast and memory-efficient algorithms
Shadi Zabad
Chirayu Anant Haryan
Simon Gravel
Sanchit Misra
Yuemei Li
2025-01-21
bioRxiv (prépublication)
doi.org
Sparse Polygenic Risk Score Inference with the Spike-and-Slab LASSO
Junyi Song
Shadi Zabad
Archer Yang
Simon Gravel
Yuemei Li
2024-12-31
Bioinform. (publié)
doi.org
Multi-ancestry polygenic risk scores using phylogenetic regularization
Elliot Layne
Shadi Zabad
Yue Li
Mathieu Blanchette
Accurately predicting phenotype using genotype across diverse ancestry groups remains a significant challenge in human genetics. Many state-… (voir plus)of-the-art polygenic risk score models are known to have difficulty generalizing to genetic ancestries that are not well represented in their training set. To address this issue, we present a novel machine learning method for fitting genetic effect sizes across multiple ancestry groups simultaneously, while leveraging prior knowledge of the evolutionary relationships among them. We introduce DendroPRS, a machine learning model where SNP effect sizes are allowed to evolve along the branches of the phylogenetic tree capturing the relationship among populations. DendroPRS outperforms existing approaches at two important genotype-to-phenotype prediction tasks: expression QTL analysis and polygenic risk scores. We also demonstrate that our method can be useful for multi-ancestry modelling, both by fitting population-specific effect sizes and by more accurately accounting for covariate effects across groups. We additionally find a subset of genes where there is strong evidence that an ancestry-specific approach improves eQTL modelling.
2024-02-16
bioRxiv (prépublication)
doi.org