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Journal of Autism and Developmental Disorders

Gepubliceerd in:

01-06-2012

Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder

Auteurs: Yun Jiao, Rong Chen, Xiaoyan Ke, Lu Cheng, Kangkang Chu, Zuhong Lu, Edward H. Herskovits

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 6/2012

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.

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Titel: Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder
Auteurs: Yun Jiao
Rong Chen
Xiaoyan Ke
Lu Cheng
Kangkang Chu
Zuhong Lu
Edward H. Herskovits
Publicatiedatum: 01-06-2012
Uitgeverij: Springer US
Gepubliceerd in: Journal of Autism and Developmental Disorders / Uitgave 6/2012
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-011-1327-5

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