Differentiating molecular etiologies of Angelman syndrome through facial phenotyping using deep learning.
Am J Med Genet A
; 182(9): 2021-2026, 2020 09.
Article
en En
| MEDLINE
| ID: mdl-32524756
ABSTRACT
Angelman syndrome (AS) is caused by several genetic mechanisms that impair the expression of maternally-inherited UBE3A through deletions, paternal uniparental disomy (UPD), UBE3A pathogenic variants, or imprinting defects. Current methods of differentiating the etiology require molecular testing, which is sometimes difficult to obtain. Recently, computer-based facial analysis systems have been used to assist in identifying genetic conditions based on facial phenotypes. We sought to understand if the facial-recognition system DeepGestalt could find differences in phenotype between molecular subtypes of AS. Images and molecular data on 261 individuals with AS ranging from 10 months through 32 years were analyzed by DeepGestalt in a cross-validation model with receiver operating characteristic (ROC) curves generated. The area under the curve (AUC) of the ROC for each molecular subtype was compared and ranked from least to greatest differentiable phenotype. We determined that DeepGestalt demonstrated a high degree of discrimination between the deletion subtype and UPD or imprinting defects, and a lower degree of discrimination with the UBE3A pathogenic variants subtype. Our findings suggest that DeepGestalt can recognize subclinical differences in phenotype based on etiology and may provide decision support for testing.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Síndrome de Angelman
/
Impresión Genómica
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Disomía Uniparental
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Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Prognostic_studies
Límite:
Adolescent
/
Adult
/
Child
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Child, preschool
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Female
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Humans
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Infant
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Male
Idioma:
En
Revista:
Am J Med Genet A
Asunto de la revista:
GENETICA MEDICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos