| Title |
Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder
|
|---|---|
| Published in |
Journal of Autism and Developmental Disorders, July 2011
|
| DOI | 10.1007/s10803-011-1327-5 |
| Pubmed ID | |
| Authors |
Yun Jiao, Rong Chen, Xiaoyan Ke, Lu Cheng, Kangkang Chu, Zuhong Lu, Edward H. Herskovits |
| 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Turkey | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 111 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 19% |
| Student > Master | 17 | 15% |
| Researcher | 16 | 14% |
| Student > Bachelor | 8 | 7% |
| Student > Postgraduate | 6 | 5% |
| Other | 14 | 12% |
| Unknown | 31 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 15 | 13% |
| Agricultural and Biological Sciences | 15 | 13% |
| Medicine and Dentistry | 13 | 12% |
| Neuroscience | 11 | 10% |
| Biochemistry, Genetics and Molecular Biology | 6 | 5% |
| Other | 20 | 18% |
| Unknown | 33 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 August 2017.
All research outputs
#14,184,606
of 23,867,274 outputs
Outputs from Journal of Autism and Developmental Disorders
#3,497
of 5,240 outputs
Outputs of similar age
#80,038
of 121,759 outputs
Outputs of similar age from Journal of Autism and Developmental Disorders
#26
of 34 outputs
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