Simulation of undiagnosed patients with novel genetic conditions
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Abstract
Rare Mendelian disorders pose a major diagnostic challenge and collectively affect 300–400 million patients worldwide. Many automated tools aim to uncover causal genes in patients with suspected genetic disorders, but evaluation of these tools is limited due to the lack of comprehensive benchmark datasets that include previously unpublished conditions. Here, we present a computational pipeline that simulates realistic clinical datasets to address this deficit. Our framework jointly simulates complex phenotypes and challenging candidate genes and produces patients with novel genetic conditions. We demonstrate the similarity of our simulated patients to real patients from the Undiagnosed Diseases Network and evaluate common gene prioritization methods on the simulated cohort. These prioritization methods recover known gene-disease associations but perform poorly on diagnosing patients with novel genetic disorders. Our publicly-available dataset and codebase can be utilized by medical genetics researchers to evaluate, compare, and improve tools that aid in the diagnostic process.
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The synthetic cohort is essential for training SHEPHERD, as it is considerably larger, more diverse, and more representative of phenotype and genotype heterogeneity than any real-world dataset of rare disease patients.
“The synthetic cohort is essential for training SHEPHERD, as it is considerably larger, more diverse, and more representative of phenotype and genotype heterogeneity than any real-world dataset of rare disease patients (Fig. 2c)48.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [48]
The Discussion (page unavailable) proposes simulated patients as training data for rare-disease machine-learning models and says they could expose models to diverse genetic disorders. The Results report a cohort of 42,680 simulated patients, and the Introduction describes simulation as scalable. The paper does not establish that this cohort is essential for training SHEPHERD or that it is more representative of phenotype and genotype heterogeneity than any real-world rare-disease dataset. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe simulated patients were generated using an approach designed to generate realistic rare disease patients grounded in medical knowledge, and they have been shown to phenotypically and genetically resemble real-world rare disease patients.
“The simulated patients were generated using an approach designed to generate realistic rare disease patients grounded in medical knowledge, and they have been shown to phenotypically and genetically resemble real-world rare disease patients48.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [48]
The simulated patients are created using an adaptive simulation approach that generates realistic rare disease patients with varying numbers of phenotype terms and candidate genes.
“The simulated patients are created using an adaptive simulation approach that generates realistic rare disease patients with varying numbers of phenotype terms and candidate genes48.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [48]
The simulated patients closely resemble real-world patients found in the UDN.
“The simulated patients closely resemble real-world patients found in the UDN.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [48]
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- Authors
- Alsentzer, E., Finlayson, S. G., Li, M. M., Undiagnosed Diseases Network, Kobren, S. N., & Kohane, I. S.
- Affiliations
- Harvard University (US)
- Seattle Children's Hospital (US)
- Corresponding author
- Kobren, Shilpa N.
- Kohane, Isaac S.
- Venue
- Nature Communications
- Published
- 2023-10-12
- Volume
- 14
- Issue
- 1
- Pages
- 6403-6403
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10570269
- OpenAlex
- W4387580247
- Cited by
- 10
- References
- 43
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 3 · 2023: 1
- FWCI
- 1.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (U54NS093793)
- Microsoft Research
- National Institutes of Health (U01TR001395, U01TR002471)
- National Human Genome Research Institute (U01HG010233, U01HG007709, U01HG007943, U01HG007703, U01HG007530, U01HG010230, U01HG007674, U01HG010217, U01HG007690, U01HG007942, U01HG007708, U01HG010219, U01HG010215, U01HG007672, T32HG002295)
- NIH Office of the Director (U01HG007709, U54 NS108251, U01HG007690, U01HG010230, U01HG007530, U01HG007708, U54 NS093793, U01HG007672, U01HG007942)
- National Institute of General Medical Sciences (T32GM007753)
- National Institute of Neurological Disorders and Stroke (U54NS093793, U54NS108251)
- National Center for Advancing Translational Sciences
- Common Fund (U01HG007530, U01TR001395, U01HG007709, U01HG007690, U01HG007672, U01HG007942, U01HG007708, U01HG007943, U54NS093793, U01 HG007703)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- rare Mendelian disorders, benchmark dataset, gene prioritization, candidate gene identification, Undiagnosed Diseases Network, gene-disease association, computational pipeline
- MeSH terms
- Computer Simulation, Humans, Patients, Phenotype, Rare Diseases
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