Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis
Open accessAbstract
PURPOSE: Despite the successful progress next-generation sequencing technologies has achieved in diagnosing the genetic cause of rare Mendelian diseases, the current diagnostic rate is still far from satisfactory because of heterogeneity, imprecision, and noise in disease phenotype descriptions and insufficient utilization of expert knowledge in clinical genetics. To overcome these difficulties, we present a novel method called Xrare for the prioritization of causative gene variants in rare disease diagnosis. METHODS: We propose a new phenotype similarity scoring method called Emission-Reception Information Content (ERIC), which is highly tolerant of noise and imprecision in clinical phenotypes. We utilize medical genetic domain knowledge by designing genetic features implementing American College of Medical Genetics and Genomics (ACMG) guidelines. RESULTS: ERIC score ranked consistently higher for disease genes than other phenotypic similarity scores in the presence of imprecise and noisy phenotypes. Extensive simulations and real clinical data demonstrated that Xrare outperforms existing alternative methods by 10-40% at various genetic diagnosis scenarios. CONCLUSION: The Xrare model is learned from a large database of clinical variants, and derives its strength from the tight integration of medical genetics features and phenotypic features similarity scores. Xrare provides the clinical community with a robust and powerful tool for variant prioritization.
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There are limited approaches for phenotype-based diagnosis, and none automatically learn phenotypic and/or genotypic features over patient phenotype terms.
“While deep learning approaches exist for image-based diagnosis10,11, there are limited approaches for phenotype-based diagnosis, and none automatically learn phenotypic and/or genotypic features over patient phenotype terms23–28,44.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [25]
The Introduction (page unavailable) names several phenotype-driven diagnostic methods, and the Discussion (page unavailable) describes Xrare as combining a phenotype similarity measure with genetic features through machine learning. These passages support the distinction between using phenotype terms and learning features from them, but they do not establish that approaches are limited or that none automatically learn features over patient phenotype terms. (LLM, conf=0.73)
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- Authors
- Li, Q., Zhao, K., Bustamante, C. D., Ma, X., & Wong, W. H.
- Affiliations
- Stanford University (US)
- Corresponding author
- Ma, Xin
- Wong, Wing H.
- Venue
- Genetics in Medicine
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 21
- Issue
- 9
- Pages
- 2126-2134
- ISSN
- 1098-3600 (ISSN-L)
- PMC
- PMC6752318
- OpenAlex
- W2911737122
- Cited by
- 106
- References
- 34
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 27 · 2023: 12 · 2022: 21 · 2021: 8 · 2020: 13 · 2019: 2
- FWCI
- 6.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Genetics in Medicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (P50-HG007735)
- National Human Genome Research Institute (P50HG007735, R01HG007834)
- National Cancer Institute (P20CA096470)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- rare disease diagnosis, monogenic disorders, variant prioritization, ACMG guidelines, genetic characteristics, medical genetics
- MeSH terms
- Machine Learning, Genetic Testing, Genotype, Humans, Mutation, Phenotype, Software, Genetic Variation, Computational Biology, Genomics, Rare Diseases, High-Throughput Nucleotide Sequencing, Exome
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