Journal of Molecular Neuroscience

, Volume 68, Issue 4, pp 515–521 | Cite as

Application of Single-Nucleotide Polymorphisms in the Diagnosis of Autism Spectrum Disorders: A Preliminary Study with Artificial Neural Networks

  • Soudeh Ghafouri-Fard
  • Mohammad TaheriEmail author
  • Mir Davood OmraniEmail author
  • Amir Daaee
  • Hossein Mohammad-Rahimi
  • Hosein Kazazi


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.


Autism spectrum disorder Artificial neural network Single-nucleotide polymorphism 



This study was financially and technically supported by Shahid Beheshti University of Medical Sciences.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. Bhat S, Dao DT, Terrillion CE, Arad M, Smith RJ, Soldatov NM, Gould TD (2012) CACNA1C (Cav1.2) in the pathophysiology of psychiatric disease. Prog Neurobiol 99(1):1–14. CrossRefGoogle Scholar
  2. Bi X-a, Liu Y, Jiang Q, Shu Q, Sun Q, Dai J (2018) The diagnosis of autism spectrum disorder based on the random neural network cluster. Front Hum Neurosci 12:257CrossRefGoogle Scholar
  3. Chen CH, Huang CC, Cheng MC, Chiu YN, Tsai WC, Wu YY, Liu SK, Gau SS (2014) Genetic analysis of GABRB3 as a candidate gene of autism spectrum disorders. Mol Autism 5:36. CrossRefGoogle Scholar
  4. Diagnostic and statistical manual of mental disorders (DSM-5®) (2013) American Psychiatric PubGoogle Scholar
  5. Elder JH, Kreider CM, Brasher SN, Ansell M (2017) Clinical impact of early diagnosis of autism on the prognosis and parent–child relationships. Psychol Res Behav Manag 10:283CrossRefGoogle Scholar
  6. Grossi E, Veggo F, Narzisi A, Compare A, Muratori F (2016) Pregnancy risk factors in autism: a pilot study with artificial neural networks. Pediatr Res 79(2):339–347. CrossRefGoogle Scholar
  7. Guo X, Dominick KC, Minai AA, Li H, Erickson CA, Lu LJ (2017) Diagnosing autism spectrum disorder from brain resting-state functional connectivity patterns using a deep neural network with a novel feature selection method. Front Neurosci 11:460. CrossRefGoogle Scholar
  8. Hamedani SY, Gharesouran J, Noroozi R, Sayad A, Omrani MD, Mir A, Afjeh SSA, Toghi M, Manoochehrabadi S, Ghafouri-Fard S, Taheri M (2017) Ras-like without CAAX 2 (RIT2): a susceptibility gene for autism spectrum disorder. Metab Brain Dis 32(3):751–755. CrossRefGoogle Scholar
  9. Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F (2018) Identification of autism spectrum disorder using deep learning and the ABIDE dataset. Neuroimage Clin 17:16–23. CrossRefGoogle Scholar
  10. Iidaka T (2015) Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex 63:55–67. CrossRefGoogle Scholar
  11. Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260CrossRefGoogle Scholar
  12. Lai YC, Kao CF, Lu ML, Chen HC, Chen PY, Chen CH, Shen WW, Wu JY, Lu RB, Kuo PH (2015) Investigation of associations between NR1D1, RORA and RORB genes and bipolar disorder. PLoS One 10(3):e0121245. CrossRefGoogle Scholar
  13. Mohammad NS, Shruti PS, Bharathi V, Prasad CK, Hussain T, Alrokayan SA, Naik U, Devi ARR (2016) Clinical utility of folate pathway genetic polymorphisms in the diagnosis of autism spectrum disorders. Psychiatr Genet 26(6):281–286. CrossRefGoogle Scholar
  14. Noroozi R, Taheri M, Movafagh A, Mirfakhraie R, Solgi G, Sayad A, Mazdeh M, Darvish H (2016) Glutamate receptor, metabotropic 7 (GRM7) gene variations and susceptibility to autism: a case–control study. Autism Res 9(11):1161–1168CrossRefGoogle Scholar
  15. Noroozi R, Taheri M, Movafagh A, Ghafouri-Fard S, Sayad A, Mirfakhraie R, Ayatollahi SA, Inoko H, Noroozi H, Do AA (2018) Association analysis of the GABRB3 promoter variant and susceptibility to autism spectrum disorder. Basal Ganglia 11:4–7CrossRefGoogle Scholar
  16. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should i trust you?: Explaining the predictions of any classifier. Paper presented at the Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data miningGoogle Scholar
  17. Safari MR, Omrani MD, Noroozi R, Sayad A, Sarrafzadeh S, Komaki A, Manjili FA, Mazdeh M, Ghaleiha A, Taheri M (2017a) Synaptosome-associated protein 25 (SNAP25) Gene Association analysis revealed risk variants for ASD, in Iranian population. J Mol Neurosci 61(3):305CrossRefGoogle Scholar
  18. Safari MR, Ghafouri-Fard S, Noroozi R, Sayad A, Omrani MD, Komaki A, Eftekharian MM, Taheri M (2017b) FOXP3 gene variations and susceptibility to autism: a case-control study. Gene 596:119–122. CrossRefGoogle Scholar
  19. Sayad A, Noroozi R, Omrani MD, Taheri M, Ghafouri-Fard S (2017) Retinoic acid-related orphan receptor alpha (RORA) variants are associated with autism spectrum disorder. Metab Brain Dis 32(5):1595–1601. CrossRefGoogle Scholar
  20. Tanaka M, Bailey JN, Bai D, Ishikawa-Brush Y, Delgado-Escueta AV, Olsen RW (2012) Effects on promoter activity of common SNPs in 5′ region of GABRB3 exon 1A. Epilepsia 53(8):1450–1456. CrossRefGoogle Scholar
  21. Ye C, Hu Z, Wu E, Yang X, Buford UJ, Guo Z, Saveanu RV (2016) Two SNAP-25 genetic variants in the binding site of multiple microRNAs and susceptibility of ADHD: a meta-analysis. J Psychiatr Res 81:56–62. CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Medical GeneticsShahid Beheshti University of Medical SciencesTehranIran
  2. 2.Urogenital Stem Cell Research CenterShahid Beheshti University of Medical SciencesTehranIran
  3. 3.School of Mechanical EngineeringSharif University of TechnologyTehranIran
  4. 4.Dental SchoolShahid Beheshti University of Medical ScienceTehranIran

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