Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging.
OBJECTIVE
Autism spectrum disorder (ASD) is a neurodevelopmental disorder, of which Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine whether a diagnostic model based on single-nucleotide polymorphisms (SNPs), brain regional thickness measurements, or brain regional volume measurements can distinguish Asperger syndrome from high-functioning autism; and 2) to compare the SNP, thickness, and volume-based diagnostic models.


METHODS
Our study included 18 children with ASD: 13 subjects with high-functioning autism and 5 subjects with Asperger syndrome. For each child, we obtained 25 SNPs for 8 ASD-related genes; we also computed regional cortical thicknesses and volumes for 66 brain structures, based on structural magnetic resonance (MR) examination. To generate diagnostic models, we employed five machine-learning techniques: decision stump, alternating decision trees, multi-class alternating decision trees, logistic model trees, and support vector machines.


RESULTS
For SNP-based classification, three decision-tree-based models performed better than the other two machine-learning models. The performance metrics for three decision-tree-based models were similar: decision stump was modestly better than the other two methods, with accuracy = 90%, sensitivity = 0.95 and specificity = 0.75. All thickness and volume-based diagnostic models performed poorly. The SNP-based diagnostic models were superior to those based on thickness and volume. For SNP-based classification, rs878960 in GABRB3 (gamma-aminobutyric acid A receptor, beta 3) was selected by all tree-based models.


CONCLUSIONS
Our analysis demonstrated that SNP-based classification was more accurate than morphometry-based classification in ASD subtype classification. Also, we found that one SNP--rs878960 in GABRB3--distinguishes Asperger syndrome from high-functioning autism.
Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China.
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Y
Jiao
R
R
Chen
X
X
Ke
L
L
Cheng
K
K
Chu
Z
Z
Lu
E H
EH
Herskovits
D001185:Artificial Intelligence; D020817:Asperger Syndrome / Q000175:diagnosis / Q000235:genetics / Q000473:pathology; D001321:Autistic Disorder / Q000175:diagnosis / Q000235:genetics / Q000473:pathology; D001921:Brain / Q000473:pathology; D002648:Child; D002659:Child Development Disorders, Pervasive / Q000175:diagnosis* / Q000235:genetics / Q000473:pathology; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests; D011963:Receptors, GABA-A / Q000235:genetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
C546273:GABRB3 protein, human; D011963:Receptors, GABA-A