A machine learning approach to predicting autism risk genes: Validation of known genes and discovery of new candidates

BioRxiv : the Preprint Server for Biology
Ying LinShizhong Han

Abstract

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role of de novo mutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be differentially expressed in ASD brains, especially in the frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example, TCF20 and FBOX11), but also indicated potentially novel candidates, such as DOCK3, MYCBP2 and CAND1, which are all involved in neuronal development. Through extensive validations, we also showed that our method outperformed state-of-the-art scoring systems for ranking ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin...Continue Reading

Related Concepts

Alternative Splicing
Brain
Gene Expression
Genes
Parietal Lobe
RNA
Signal Pathways
Patterns
Proteolysis
Neurogenesis

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