A Machine Learning Approach to Predicting Autism Risk Genes: Validation of Known Genes and Discovery of New Candidates.

Ying Lin,Anjali M Rajadhyaksha,James B Potash,Shiva Afshar,Shizhong Han

doi:10.3389/fgene.2020.500064

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 down-expressed in ASD brains, especially in frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example, NBEA, HERC1, and TCF20), but also indicated potentially novel candidates, such as, MYCBP2 and CAND1, which are involved in protein ubiquitination. We also showed that our method outperformed state-of-the-art scoring systems for ranking curated 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 remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.

Highlights

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social interaction and communication, as well as repetitive behavior
The basic premise is that ASD risk genes tend to show distinguishing features, including spatial-temporal gene expression patterns in human brain, gene network features, and gene-level constraint metrics
To evaluate the performance of our prediction model, we examined if predicted ASD genes were enriched for differential gene expression (DGE) evidence and independent sets of ASD risk genes

Summary

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social interaction and communication, as well as repetitive behavior. De novo Mutations and Autism gene (Wang et al, 2016; Stessman et al, 2017), whole exome (Iossifov et al, 2012, 2014; Sanders et al, 2012; Ronemus et al, 2014), and whole genome sequencing studies (Ryan et al, 2017; Turner et al, 2017). The list of risk genes implicated by de novo mutations is growing, it is still very likely far from complete, with an estimated full set of ASD genes ranging from several hundred to more than 1,000 (Iossifov et al, 2014). In the search for additional de novo mutations, sequencing studies continue to be an important approach, but the current sequencing cost is still very high, especially for large samples. As an alternative strategy, advanced analytical approaches, which leverage previously implicated genes and prior knowledge, have the potential to enhance risk gene discovery in an efficient and cost-effective manner (Asif et al, 2018; Gök, 2018; Brueggeman et al, 2020)

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