ERIC Number: EJ967933
Record Type: Journal
Publication Date: 2012-Jun
Abstractor: As Provided
Reference Count: 55
Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder
Jiao, Yun; Chen, Rong; Ke, Xiaoyan; Cheng, Lu; Chu, Kangkang; Lu, Zuhong; Herskovits, Edward H.
Journal of Autism and Developmental Disorders, v42 n6 p971-983 Jun 2012
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.
Descriptors: Autism, Rating Scales, Etiology, Severity (of Disability), Teaching Methods, Predictor Variables, Symptoms (Individual Disorders), Pervasive Developmental Disorders, Genetics, Children, Classification
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Childhood Autism Rating Scale