Support vector machine model of developmental brain gene expression data for prioritization of Autism risk gene candidates

Motivation: Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders with clinical heterogeneity and a substantial polygenic component. High-throughput methods for ASD risk gene identification produce numerous candidate genes that are time-consuming and expensive to validate. Prioritization methods can identify high-confidence candidates. Previous ASD gene prioritization methods have focused on a priori knowledge, which excludes genes with little functional annotation or no protein product such as long non-coding RNAs (lncRNAs). Results: We have developed a support vector machine (SVM) model, trained using brain developmental gene expression data, for the classification and prioritization of ASD risk genes. The selected feature model had a mean accuracy of 76.7%, mean specificity of 77.2% and mean sensitivity of 74.4%. Gene lists comprised of an ASD risk gene and adjacent genes were ranked using the model's decision function output. The known ASD risk genes were ranked on average in the 77.4th, 78.4th and 80.7th percentile for sets of 101, 201 and 401 genes respectively. Of 10,840 lncRNA genes, 63 were classified as ASD-associated candidates with a confidence greater than 0.95. Genes previously associated with brain development and neurodevelopmental disorders were prioritized highly within the lncRNA gene list.