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Lookup NU author(s): Dr David Lewis-Smith
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2021, The Author(s). While genetic studies of epilepsies can be performed in thousands of individuals, phenotyping remains a manual, non-scalable task. A particular challenge is capturing the evolution of complex phenotypes with age. Here, we present a novel approach, applying phenotypic similarity analysis to a total of 3251 patient-years of longitudinal electronic medical record data from a previously reported cohort of 658 individuals with genetic epilepsies. After mapping clinical data to the Human Phenotype Ontology, we determined the phenotypic similarity of individuals sharing each genetic etiology within each 3-month age interval from birth up to a maximum age of 25 years. 140 of 600 (23%) of all 27 genes and 3-month age intervals with sufficient data for calculation of phenotypic similarity were significantly higher than expect by chance. 11 of 27 genetic etiologies had significant overall phenotypic similarity trajectories. These do not simply reflect strong statistical associations with single phenotypic features but appear to emerge from complex clinical constellations of features that may not be strongly associated individually. As an attempt to reconstruct the cognitive framework of syndrome recognition in clinical practice, longitudinal phenotypic similarity analysis extends the traditional phenotyping approach by utilizing data from electronic medical records at a scale that is far beyond the capabilities of manual phenotyping. Delineation of how the phenotypic homogeneity of genetic epilepsies varies with age could improve the phenotypic classification of these disorders, the accuracy of prognostic counseling, and by providing historical control data, the design and interpretation of precision clinical trials in rare diseases.
Author(s): Lewis-Smith D, Ganesan S, Galer PD, Helbig KL, McKeown SE, O'Brien M, Khankhanian P, Kaufman MC, Gonzalez AK, Felmeister AS, Krause R, Ellis CA, Helbig I
Publication type: Article
Publication status: Published
Journal: European Journal of Human Genetics
Print publication date: 01/11/2021
Online publication date: 24/05/2021
Acceptance date: 04/05/2021
Date deposited: 18/06/2021
ISSN (print): 1018-4813
ISSN (electronic): 1476-5438
Publisher: Springer Nature
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