Single nucleotide polymorphisms predict symptom severity of autism spectrum disorder.
J Autism Dev Disord
; 42(6): 971-83, 2012 Jun.
Article
en En
| MEDLINE
| ID: mdl-21786105
ABSTRACT
Autism is widely believed to be a heterogeneous disorder; diagnosis is currently based solely on clinical criteria, although genetic, as well as environmental, influences are thought to be prominent factors in the etiology of most forms of autism. Our goal is to determine whether a predictive model based on single-nucleotide polymorphisms (SNPs) can predict symptom severity of autism spectrum disorder (ASD). We divided 118 ASD children into a mild/moderate autism group (n = 65) and a severe autism group (n = 53), based on the Childhood Autism Rating Scale (CARS). For each child, we obtained 29 SNPs of 9 ASD-related genes. To generate predictive models, we employed three machine-learning techniques decision stumps (DSs), alternating decision trees (ADTrees), and FlexTrees. DS and FlexTree generated modestly better classifiers, with accuracy = 67%, sensitivity = 0.88 and specificity = 0.42. The SNP rs878960 in GABRB3 was selected by all models, and was related associated with CARS assessment. Our results suggest that SNPs have the potential to offer accurate classification of ASD symptom severity.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Índice de Severidad de la Enfermedad
/
Trastornos Generalizados del Desarrollo Infantil
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Polimorfismo de Nucleótido Simple
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adolescent
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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:
J Autism Dev Disord
Año:
2012
Tipo del documento:
Article
País de afiliación:
China