Hidden Transcriptome Signature in Autism Spectrum Disorder (ASD) is Attributed to the Interpatient Heterogeneity

Abstract

Recent studies of ASD have mostly supported the existence of heterogeneity and genomic variation in ASD which have hindered and restrained development of any effective and targetable treatment for a long time. As numerous studies have shown, both genetic and phenotypic heterogeneity is presented in ASD, however, heterogeneity in genetic level is not fully understood which is the key challenges for the further research. Even dozens of ASD susceptibility genes have been discovered which is commonly accounting for 10 to 20 percent of ASD cases, the internal complex combination of mutated genes that determine the epigenetic factors of ASD is still not comprehensively recognized by the recent studies. First by discouraging the traditional method that have been applied in most of the current research of diseases, this research will then focus on dissecting the heterogeneity of polygenic diseases and analyzing with an unconventional approach for acquiring Differently Expressed Genes (DEGs) in Guptas Dataset that provided transcriptome of frontal cortex of ASD patients. Divide categories by using unsupervised learning strategy, the results yielded by analyzing within clusters of ASD have supported the feasibility of the attempts to use heterogeneity to reveal its underlying mechanism. This study puts forward the inference that the heterogeneity of polygenic diseases will obscure the molecular signals related to the disease, and at the same time attempts to use heterogeneity to reveal the underlying mechanism.