Application of single-nucleotide polymorphisms in the diagnosis of autism spectrum disorders: a preliminary study with artificial neural networks

Ghafouri Fard, S ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1007/s12031-019-01311-1
  3. Publisher: Springer New York LLC , 2019
  4. Abstract:
  5. Autism spectrum disorder (ASD) includes different neurodevelopmental disorders characterized by deficits in social communication, and restricted, repetitive patterns of behavior, interests or activities. Based on the importance of early diagnosis for effective therapeutic intervention, several strategies have been employed for detection of the disorder. The artificial neural network (ANN) as a type of machine learning method is a common strategy. In the current study, we extracted genomic data for 487 ASD patients and 455 healthy individuals. All individuals were genotyped in certain single-nucleotide polymorphisms within retinoic acid-related orphan receptor alpha (RORA), gamma-aminobutyric acid type A receptor beta3 subunit (GABRB3), synaptosomal-associated protein 25 (SNAP25) and metabotropic glutamate receptor 7 (GRM7) genes. Subsequently, we used the “Keras” package to create and train the ANN model. For cross-validation, samples were divided into ten folds. In the training process, initially, the first fold was preserved for validation and the other folds were used to train the model. The validation fold was then used to evaluate model performance. The k-fold cross-validation method was used to ensure model generalizability and to prevent overfitting. Local interpretable model-agnostic explanations (LIME) were applied to explain model predictions at the data sample level. The output of loss function was evaluated in the training process for each fold in the k-fold cross-validation model. Finally, the number of losses was reduced to less than 0.6 after 200 epochs (except in two cases). The accuracy, sensitivity and specificity of our model were 73.67%, 82.75% and 63.95%, respectively. The area under the curve (AUC) was 80.59. Consequently, in the current study, we propose an ANN-based method for differentiating ASD status from healthy status with adequate power. © 2019, Springer Science+Business Media, LLC, part of Springer Nature
  6. Keywords:
  7. Artificial neural network ; Autism spectrum disorder ; Single-nucleotide polymorphism ; Article ; Controlled study ; Diagnostic accuracy ; Diagnostic test accuracy study ; Gabrb3 gene ; Genomics ; Genotype ; Grm7 gene ; Human ; Major clinical study ; Receptor gene ; Rora gene ; Single nucleotide polymorphism ; Snap25 gene ; Autism ; Genetic screening ; Genetics ; Procedures ; 4 aminobutyric acid A receptor ; GABRB3 protein, human ; Metabotropic receptor ; Metabotropic receptor 7 ; SNAP25 protein, human ; Synaptosomal associated protein 25 ; Child ; Deep Learning ; Female ; Genetic Testing ; Humans ; Male ; Polymorphism, Single Nucleotide ; Receptors, GABA-A ; Receptors, Metabotropic Glutamate ; Sensitivity and Specificity ; Synaptosomal-Associated Protein 25
  8. Source: Journal of Molecular Neuroscience ; Volume 68, Issue 4 , 2019 , Pages 515-521 ; 08958696 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s12031-019-01311-1