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Personalised analytics for rare disease diagnostics
Open accessAbstract
Whole genome and exome sequencing is a standard tool for the diagnosis of patients suffering from rare and other genetic disorders. The interpretation of the tens of thousands of variants returned from such tests remains a major challenge. Here we focus on the problem of prioritising variants with respect to the observed disease phenotype. We hypothesise that linking patterns of gene expression across multiple tissues to the phenotypes will aid in discovering disease causing variants. To test this, we construct classifiers that learn associations between tissue-specific gene expression and disease phenotypes. We find that using Genotype-Tissue Expression project (GTEx) expression data in conjunction with disease agnostic variant prioritisation methods (CADD or MetaSVM) results in consistent improvements in classification accuracy. Our method represents a previously overlooked avenue of utilising existing expression data for clinical diagnostics, and also opens the door to use of other functional genomic data sets in the same manner.
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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 [28]
The Introduction (page not provided) describes VARPP as random-forest classifiers built for 1,879 Human Phenotype Ontology terms using variant scores and gene-expression features. The Abstract (page not provided) says these classifiers learn associations between gene expression and disease phenotypes. Thus, while the paper discusses limitations of existing variant-prioritisation tools, its own approach runs counter to the claim that none learn such features by phenotype term. (LLM, conf=0.84)
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References 44 · Cited by 24
Research works printed but not linked: 1
- Ouwehand, W. H. Whole-genome sequencing of rare disease patients in a national healthcare system. bioRxiv, 507244. Preprint at https://doi.org/10.1101/507244v507241 (2019). · DOI
Metadata
- Authors
- Anderson, D., Baynam, G., Blackwell, J. M., & Lassmann, T.
- Affiliations
- The Kids Research Institute Australia (AU)
- Government of Western Australia Department of Health (AU)
- Corresponding author
- Anderson, Denise
- Lassmann, Timo
- Venue
- Nature Communications
- Published
- 2019-11-21
- Volume
- 10
- Issue
- 1
- Pages
- 5274-5274
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6872807
- OpenAlex
- W2989793717
- Cited by
- 24
- References
- 44
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 3 · 2023: 1 · 2022: 5 · 2021: 8 · 2020: 1
- FWCI
- 2.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- rare disease diagnosis, variant prioritization, tissue-specific gene expression, GTEx, CADD, whole genome sequencing, exome sequencing, functional genomic data, clinical diagnostics
- MeSH terms
- Exome Sequencing, Gene Expression Regulation, Genotype, Humans, Phenotype, Genetic Variation, Genome, Human, Gene Expression Profiling, Rare Diseases, Genome-Wide Association Study, Precision Medicine, High-Throughput Nucleotide Sequencing
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