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Rare disorders have many faces: in silico characterization of rare disorder spectrum Frederiksen, Simona D.; Avramović, Vladimir; Maroilley, Tatiana; Lehman, Anna; Arbour, Laura; Tarailo-Graovac, Maja
Abstract
Background The diagnostic journey for many rare disease patients remains challenging despite use of latest genetic technological advancements. We hypothesize that some patients remain undiagnosed due to more complex diagnostic scenarios that are currently not considered in genome analysis pipelines. To better understand this, we characterized the rare disorder (RD) spectrum using various bioinformatics resources (e.g., Orphanet/Orphadata, Human Phenotype Ontology, Reactome pathways) combined with custom-made R scripts. Results Our in silico characterization led to identification of 145 borderline-common, 412 rare and 2967 ultra-rare disorders. Based on these findings and point prevalence, we would expect that approximately 6.53%, 0.34%, and 0.30% of individuals in a randomly selected population have a borderline-common, rare, and ultra-rare disorder, respectively (equaling to 1 RD patient in 14 people). Importantly, our analyses revealed that (1) a higher proportion of borderline-common disorders were caused by multiple gene defects and/or other factors compared with the rare and ultra-rare disorders, (2) the phenotypic expressivity was more variable for the borderline-common disorders than for the rarer disorders, and (3) unique clinical characteristics were observed across the disorder categories forming the spectrum. Conclusions Recognizing that RD patients who remain unsolved even after genome sequencing might belong to the more common end of the RD spectrum support the usage of computational pipelines that account for more complex genetic and phenotypic scenarios.
Item Metadata
Title |
Rare disorders have many faces: in silico characterization of rare disorder spectrum
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Creator | |
Publisher |
BioMed Central
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Date Issued |
2022-02-22
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Description |
Background
The diagnostic journey for many rare disease patients remains challenging despite use of latest genetic technological advancements. We hypothesize that some patients remain undiagnosed due to more complex diagnostic scenarios that are currently not considered in genome analysis pipelines. To better understand this, we characterized the rare disorder (RD) spectrum using various bioinformatics resources (e.g., Orphanet/Orphadata, Human Phenotype Ontology, Reactome pathways) combined with custom-made R scripts.
Results
Our in silico characterization led to identification of 145 borderline-common, 412 rare and 2967 ultra-rare disorders. Based on these findings and point prevalence, we would expect that approximately 6.53%, 0.34%, and 0.30% of individuals in a randomly selected population have a borderline-common, rare, and ultra-rare disorder, respectively (equaling to 1 RD patient in 14 people). Importantly, our analyses revealed that (1) a higher proportion of borderline-common disorders were caused by multiple gene defects and/or other factors compared with the rare and ultra-rare disorders, (2) the phenotypic expressivity was more variable for the borderline-common disorders than for the rarer disorders, and (3) unique clinical characteristics were observed across the disorder categories forming the spectrum.
Conclusions
Recognizing that RD patients who remain unsolved even after genome sequencing might belong to the more common end of the RD spectrum support the usage of computational pipelines that account for more complex genetic and phenotypic scenarios.
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Subject | |
Genre | |
Type | |
Language |
eng
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Date Available |
2022-03-25
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution 4.0 International (CC BY 4.0)
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DOI |
10.14288/1.0407322
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URI | |
Affiliation | |
Citation |
Orphanet Journal of Rare Diseases. 2022 Feb 22;17(1):76
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Publisher DOI |
10.1186/s13023-022-02217-9
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Peer Review Status |
Reviewed
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Scholarly Level |
Faculty; Researcher
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Copyright Holder |
The Author(s)
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution 4.0 International (CC BY 4.0)