Computational Analysis of Autism Spectrum Disorder Biomarkers

Abstract

Autism spectrum disorder (ASD) is one of the most common neurodevelopmental disorders. Worldwide, ASD tends to have a prevalence of one in 100 children, with an estimated prevalence of 1 in 44 children, according to CDC’s Autism and Developmental Disabilities Monitoring Network. To date, no effective medical/FDA treatments for the core symptoms of ASD exist. However, biomarkers capable of detecting and diagnosing ASD can help to translate experimental research results to bench side clinical practices. Biomarker discovery in ASD is complicated by the diversity of core symptoms which comprise deficits in social communication, presence of rigid, repetitive and stereotypical behaviors, and comorbid medical (e.g., epilepsy) or psychiatric symptoms. The EU-AIMS Longitudinal European Autism Project (LEAP), the largest consortia made a great advancement in the discovery of biomarkers for ASD. It seeks to identify stratification biomarkers using neurobiological or neurocognitive measures, neuroimaging, electrophysiology, biochemistry, and genetics. This work is aimed at the identification of single nucleotide polymorphisms (SNPs) based on SNP genotyping in genomic DNA in a large cohort of ASD patients and unaffected related individuals. We hypothesized that calculating distance of alleles between affected and unaffected population using the Cartesian distance in the space of the alleles frequencies we can identify new putative biomarkers. The dataset retrieved from the Gene Expression Omnibus database (GSE6754) contains more than 6000 samples from 1,400 families. Our studies propose that the SNPs that are ranked by the distance in three-dimensional genotype count space between all the affected and unaffected subjects in the cohort are likely to be linked to ASD. These results will open new doors for further investigation and future work is expected to help identify the exact genetic mechanisms of ASD. Based on the distance between patients and healthy relatives in the space of mutated alleles, we decided to focus on Fragile X syndrome which is known to be the single leading cause of inherited intellectual disability and autism spectrum disorder. It is found to be on the 1010 top-ranking SNPs targeted in the unbalanced cohort. By performing in silico analysis using computational and other bioinformatics tool which provides a powerful approach to gain a better understanding of the biological systems at the gene/protein level.