TY - JOUR T1 - Aberrant gene expression in autism JF - bioRxiv DO - 10.1101/029488 SP - 029488 AU - Jinting Guan AU - Ence Yang AU - Jizhou Yang AU - Gang Wang AU - Yong Zeng AU - Guoli Ji AU - James J. Cai Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/10/20/029488.abstract N2 - Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by substantial phenotypic and genetic heterogeneity. Analyzing gene expression may reveal specific patterns of gene activation and repression associated with the disorder. For this, we developed a new analysis method based on a multivariate distance measure commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between populations and allows the identification of aberrantly expressed genes in a two-group setting. 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. The expression dispersion of these genes is more pronounced in autistics than controls. Many of these genes are known to be implicated in ASD, including those expressed in the synapse and those with neuropeptide binding function. Furthermore, we detected co-expressed gene modules among non-autistic controls and found that many of these modules disappear among autistics, due to the aberrant gene expression exclusively affecting ASD subjects. For the diagnostic purposes, we developed 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. We identified three classifier gene sets producing prominent and specific signals, which allowed the distinguishing of ASD from non-ASD samples. In summary, we have developed the aberrant gene expression analysis based on the multivariate, dispersion-specific measure, which is powerful in detecting increased gene expression variability functionally associated with ASD. Detecting aberrant gene expression and identifying underlying genes and mutations represent a new discovery and diagnostic strategy for genetically heterogeneous disorders like ASD.Author Summary There is substantial phenotypic and genetic heterogeneity in autism, which complicates studies seeking to identify genetic factors that contribute to the disorder. However, despite this complexity, study designs have focused on using group differences, e.g., in gene expression, between autistic cases and controls to identify the genetic effects. The problem is that, by their nature, group difference-based methods, such as differential expression (DE) analysis, blur or collapse the heterogeneity within autistics, which, in fact, characterizes this spectrum disorder. For instance, DE analysis identifies genes expressed differentially between groups, but, by its design, the method tends to identify genes expressed uniformly or less variably across individuals within groups. By ignoring genes with variable expression in autistic individuals, an important axis of genetic heterogeneity contributing to gene expression variability among affected individuals has been overlooked. We have developed a new analysis method called aberrant gene expression analysis, aimed to identify genes with significant changes in expression variance between diseased and non-diseased samples. This is in sharp contrast to the purpose of conventional DE analysis method, which aims to identify genes with significant changes in expression mean. Using this new method, we detected increased gene expression variability and identified candidate genes with functions relevant to autism. ER -