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Nat. Genet., 03 November 2025 | https://doi.org/10.1038/s41588-025-02370-4
Liability threshold model-based disease risk prediction based on electronic health record phenotypes
Cue Hyunkyu Lee1, Atlas Khan2, Chen Wang3, Chunhua Weng3, Joseph D. Buxbaum4, Krzysztof Kiryluk2 & Iuliana Ionita-Laza1,5
1Department of Biostatistics, Columbia University, New York, NY, USA.
2Department of Medicine, Columbia University, New York, NY, USA.
3Department of Biomedical Informatics, Columbia University, New York, NY, USA.
4Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
5Department of Statistics, Lund University, Lund, Sweden.
Corresponding author
Correspondence to Iuliana Ionita-Laza.
1Department of Biostatistics, Columbia University, New York, NY, USA.
2Department of Medicine, Columbia University, New York, NY, USA.
3Department of Biomedical Informatics, Columbia University, New York, NY, USA.
4Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
5Department of Statistics, Lund University, Lund, Sweden.
Corresponding author
Correspondence to Iuliana Ionita-Laza.
Abstract
Electronic health records have been increasingly adopted as useful resources for genomic research. However, case–control labeling of clinical data from electronic health records is challenging and most studies utilize phenotype codes to define case/control labels, resulting in suboptimal downstream analyses. Here we describe the liability threshold phenotypic integration, a method combining genetic relatedness with phenotypic data, including binary and continuous traits such as diagnosis codes, family disease history, laboratory measurements and biomarkers, to derive new continuous phenotypes for target diseases. The model utilizes an automatic trait selection algorithm that increases performance in disease risk prediction and provides insights into nontarget traits associated with the target disease. Our simulations and applications to the eMERGE network and the UK Biobank data demonstrate consistent performance gains in disease risk prediction and genome-wide association study power compared to conventional phenotype codes, models that solely incorporate family history and the phenotype imputation method SoftImpute, with similar false-positive rate control.
논문정보
- Research article
- 2025년 11월 (BRIC 등록일 2025-11-05)
- 국외 연구진
- 생명과학 > 유전학
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