Text-mined phenotype annotation and vector-based similarity to improve identification of similar phenotypes and causative genes in monogenic disease patients.
Hum Mutat
; 39(5): 643-652, 2018 05.
Article
en En
| MEDLINE
| ID: mdl-29460986
ABSTRACT
The genetic diagnosis of rare monogenic diseases using exome/genome sequencing requires the true causal variant(s) to be identified from tens of thousands of observed variants. Typically a virtual gene panel approach is taken whereby only variants in genes known to cause phenotypes resembling the patient under investigation are considered. With the number of known monogenic gene-disease pairs exceeding 5,000, manual curation of personalized virtual panels using exhaustive knowledge of the genetic basis of the human monogenic phenotypic spectrum is challenging. We present improved probabilistic methods for estimating phenotypic similarity based on Human Phenotype Ontology annotation. A limitation of existing methods for evaluating a disease's similarity to a reference set is that reference diseases are typically represented as a series of binary (present/absent) observations of phenotypic terms. We evaluate a quantified disease reference set, using term frequency in phenotypic text descriptions to approximate term relevance. We demonstrate an improved ability to identify related diseases through the use of a quantified reference set, and that vector space similarity measures perform better than established information content-based measures. These improvements enable the generation of bespoke virtual gene panels, facilitating more accurate and efficient interpretation of genomic variant profiles from individuals with rare Mendelian disorders. These methods are available online at https//atlas.genetics.kcl.ac.uk/~jake/cgi-bin/patient_sim.py.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Enfermedad
/
Predisposición Genética a la Enfermedad
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Bases de Datos Genéticas
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Minería de Datos
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Curaduría de Datos
Tipo de estudio:
Diagnostic_studies
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Guideline
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Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Hum Mutat
Asunto de la revista:
GENETICA MEDICA
Año:
2018
Tipo del documento:
Article
País de afiliación:
Reino Unido