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Autism spectrum disorder (ASD) refers to a group of childhood neurodevelopmental disorders with polygenic etiology. The expression of many genes implicated in ASD is tightly regulated by various factors including microRNAs (miRNAs), a class of noncoding RNAs ~22 nucleotides in length that function to suppress translation by pairing with 'miRNA recognition elements' (MREs) present in the 3'untranslated region (3'UTR) of target mRNAs. This emphasizes the role played by miRNAs in regulating neurogenesis, brain development and differentiation and hence any perturbations in this regulatory mechanism might affect these processes as well. Recently, single nucleotide polymorphisms (SNPs) present within 3'UTRs of mRNAs have been shown to modulate existing MREs or even create new MREs. Therefore, we hypothesized that SNPs perturbing miRNA-mediated gene regulation might lead to aberrant expression of autism-implicated genes, thus resulting in disease predisposition or pathogenesis in at least a subpopulation of ASD individuals. We developed a systematic computational pipeline that integrates data from well-established databases. By following a stringent selection criterion, we identified 9 MRE-modulating SNPs and another 12 MRE-creating SNPs in the 3'UTR of autism-implicated genes. These high-confidence candidate SNPs may play roles in ASD and hence would be valuable for further functional validation.

Mining the 3′UTR of Autism-Implicated Genes for SNPs Perturbing MicroRNA Regulation