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Individualised human phenotype ontology gene panels improve clinical whole exome and genome sequencing analytical efficacy in a cohort of developmental and epileptic encephalopathies

BACKGROUND: The majority of genetic epilepsies remain unsolved in terms of specific genotype. Phenotype‐based genomic analyses have shown potential to strengthen genomic analysis in various ways, including improving analytical efficacy. METHODS: We have tested a standardised phenotyping method terme...

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Detalles Bibliográficos
Autores principales: Henry, Olivia J., Stödberg, Tommy, Båtelson, Sofia, Rasi, Chiara, Stranneheim, Henrik, Wedell, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337286/
https://www.ncbi.nlm.nih.gov/pubmed/36967109
http://dx.doi.org/10.1002/mgg3.2167
Descripción
Sumario:BACKGROUND: The majority of genetic epilepsies remain unsolved in terms of specific genotype. Phenotype‐based genomic analyses have shown potential to strengthen genomic analysis in various ways, including improving analytical efficacy. METHODS: We have tested a standardised phenotyping method termed ‘Phenomodels’ for integrating deep‐phenotyping information with our in‐house developed clinical whole exome/genome sequencing analytical pipeline. Phenomodels includes a user‐friendly epilepsy phenotyping template and an objective measure for selecting which template terms to include in individualised Human Phenotype Ontology (HPO) gene panels. In a pilot study of 38 previously solved cases of developmental and epileptic encephalopathies, we compared the sensitivity and specificity of the individualised HPO gene panels with the clinical epilepsy gene panel. RESULTS: The Phenomodels template showed high sensitivity for capturing relevant phenotypic information, where 37/38 individuals' HPO gene panels included the causative gene. The HPO gene panels also had far fewer variants to assess than the epilepsy gene panel. CONCLUSION: We have demonstrated a viable approach for incorporating standardised phenotype information into clinical genomic analyses, which may enable more efficient analysis.