Meta-analysis of whole-transcriptome data for prediction of novel genes associated with autism spectrum disorder

Autism spectrum disorder (ASD) is a common heterogeneous neurodevelopmental disorder with typical symptoms such as impaired social interaction, language and communication abnormalities and stereotypical behavior. Since the genetics of ASDs is so diverse, information on genome function as provided by transcriptomic data is essential to further our understanding. This is transcriptome is a key link between measuring protein levels and genetic information. Transcriptome-based studies have been often performed by comparing ASD and control groups to identify which genes are dysregulated in the ASD group using statistical techniques. However, these statistical techniques can only find genes solely related to ASD, but cannot reflect relationship among genes which could be the etiology of ASD. In this study, we propose a novel method to find the ASD-associated genes, which are predictive for ASD. For this purpose, we metaanalyze whole-transcriptomic data of previous studies for ASD, which were performed using some expression profiling platforms on different issues of interest. These predictive genes, which can differentiate a sample into either ASD or non-ASD, are selected by an optimization process. Comparing subsets selected from different tissues/platforms, we conclude that tissues contain different gene sets associated with ASD. In addition, a platform can supply other ASD-associated genes of which other platforms cannot. Identified genes are then compared to those which have been well documented in SFARI, which is the most comprehensive and up-to-date data of ASD. Interestingly, we can find two novel genes with evidences from literature, which have not yet been recorded in this database. In summary, meta-analysis on whole-transcriptome data of ASD could shed light on the etiology of ASD.