Journal of Autism and Developmental Disorders

, Volume 42, Issue 6, pp 971–983

Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder


DOI: 10.1007/s10803-011-1327-5

Cite this article as:
Jiao, Y., Chen, R., Ke, X. et al. J Autism Dev Disord (2012) 42: 971. doi:10.1007/s10803-011-1327-5


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.


Autism-spectrum disorderSingle-nucleotide polymorphismsDiagnostic modelGenotype-phenotype analysisData mining

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Key Laboratory of Child Development and Learning Science, Ministry of EducationSoutheast UniversityNanjingChina
  2. 2.State Key Laboratory of Bioelectronics, School of Biological Science and Medical EngineeringSoutheast UniversityNanjingChina
  3. 3.Department of RadiologyUniversity of Pennsylvania School of MedicinePhiladelphiaUSA
  4. 4.Child Mental Health Research Center of Nanjing Brain HospitalNanjing Medical UniversityNanjingChina