Exome sequencing and copy number variation analyses continue to provide novel insight to the biological bases of autism spectrum disorder (ASD). The growing speed at which massive genetic data are produced causes serious lags in analysis and interpretation of the data. Thus, there is a need to develop systematic genetic data mining processes that facilitate efficient analysis of large datasets. We report a new genetic data mining system, ProcessGeneLists and integrated a list of ASD-related genes with currently available resources in gene expression and functional connectivity of the human brain. Our data-mining program successfully identified three primary regions of interest (ROIs) in the mouse brain: inferior colliculus, ventral posterior complex of the thalamus (VPC), and parafascicular nucleus (PFn). To understand its pathogenic relevance in ASD, we examined the resting state functional connectivity (RSFC) of the homologous ROIs in human brain with other brain regions that were previously implicated in the neuro-psychiatric features of ASD. Among them, the RSFC of the VPC with the medial frontal gyrus (MFG) was significantly more anticorrelated, whereas the RSFC of the PN with the globus pallidus was significantly increased in children with ASD compared with healthy children. Moreover, greater values of RSFC between VPC and MFG were correlated with severity index and repetitive behaviors in children with ASD. No significant RSFC differences were detected in adults with ASD. Together, these data demonstrate the utility of our data-mining program through identifying the aberrant connectivity of thalamo-cortical circuits in children with ASD.
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