Aberrant gene expression in autism

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by phenotypic and genetic heterogeneity. Analyzing gene expression in autistic brains may reveal specific patterns of gene activation and repression associated with the disorder. For this, we developed a new analysis method called aberrant gene expression analysis, based on a multivariate distance measure for outlier detection, to identify aberrantly expressed genes. In a two-group setting, our method detects the discrepancies in gene expression dispersion between populations. Using this method, we re-visited RNA sequencing data previously generated from post-mortem brain tissues of 47 autistic and 57 control samples and identified a number of sets of genes with increased expression variability in autistics. In other words, the expression dispersion of these genes in autistics is more pronounced than that in controls. Many of those genes such as those expressed in synapse and those involved in neuropeptide binding are known to be implicated in ASD. We also found that many co-expressed gene modules present among non-autistic controls disappear among autistics, due to the aberrant gene expression that exclusively affects ASD patients. For the diagnostic purposes, we used a greedy algorithm to globally search for classifier gene sets, for which the expression in peripheral blood samples of autistics is maximally deviant from that of non-autistic controls. The aberrant gene expression respecting the classifier gene sets is more pronounced and specific for ASD samples, allowing the distinguishing of ASD from non-ASD samples. These results suggest that aberrant gene expression has the potential to be used as biomarkers for ASD. Taken together, we have developed a new gene expression analysis method based on a multivariate, dispersion-specific measure, which is powerful in detecting increased gene expression variability functionally associated with ASD. We conclude that detecting aberrant gene expression and identifying the underlying genes represent a new discovery and diagnostic strategy for ASD and other genetically heterogeneous disorders.