Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder

Autor/inn/en	Jiao, Yun; Chen, Rong; Ke, Xiaoyan; Cheng, Lu; Chu, Kangkang; Lu, Zuhong; Herskovits, Edward H.
Titel	Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder
Quelle	In: Journal of Autism and Developmental Disorders, 42 (2012) 6, S. 971-983 PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0162-3257
DOI	10.1007/s10803-011-1327-5
Schlagwörter	Forschungsbericht; Autism; Rating Scales; Etiology; Severity (of Disability); Teaching Methods; Predictor Variables; Symptoms (Individual Disorders); Pervasive Developmental Disorders; Genetics; Children; Classification; Childhood Autism Rating Scale + Suchen Sie Ihr Suchwort? Autismus; Rating-Skala; Ätiologie; Schweregrad; Teaching method; Lehrmethode; Unterrichtsmethode; Prädiktor; Psychiatrische Symptomatik; Humangenetik; Child; Kind; Kinder; Classification system; Klassifikation; Klassifikationssystem
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. (As Provided).
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Begutachtung	Peer reviewed
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10