Predicting the Risk Genes of Autism Spectrum Disorders.

Yenching Lin,Srinivasulu Yerukala Sathipati,Shinn-Ying Ho

doi:10.3389/fgene.2021.665469

Abstract

Autism spectrum disorder (ASD) refers to a wide spectrum of neurodevelopmental disorders that emerge during infancy and continue throughout a lifespan. Although substantial efforts have been made to develop therapeutic approaches, core symptoms persist lifelong in ASD patients. Identifying the brain temporospatial regions where the risk genes are expressed in ASD patients may help to improve the therapeutic strategies. Accordingly, this work aims to predict the risk genes of ASD and identify the temporospatial regions of the brain structures at different developmental time points for exploring the specificity of ASD gene expression in the brain that would help in possible ASD detection in the future. A dataset consisting of 13 developmental stages ranging from 8 weeks post-conception to 8 years from 26 brain structures was retrieved from the BrainSpan atlas. This work proposes a support vector machine–based risk gene prediction method ASD-Risk to distinguish the risk genes of ASD and non-ASD genes. ASD-Risk used an optimal feature selection algorithm called inheritable bi-objective combinatorial genetic algorithm to identify the brain temporospatial regions for prediction of the risk genes of ASD. ASD-Risk achieved a 10-fold cross-validation accuracy, sensitivity, specificity, area under a receiver operating characteristic curve, and a test accuracy of 81.83%, 0.84, 0.79, 0.84, and 72.27%, respectively. We prioritized the temporospatial features according to their contribution to the prediction accuracy. The top identified temporospatial regions of the brain for risk gene prediction included the posteroventral parietal cortex at 13 post-conception weeks feature. The identified temporospatial features would help to explore the risk genes that are specifically expressed in different brain regions of ASD patients.

Highlights

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a repetitive behavior, difficulty in communication, and deceit in social interaction
This study proposed an support vector machine (SVM)-based classifier, ASD-Risk, to categorize the risk genes of ASD and identify the temporospatial regions of the brain using gene expression profiles that are implicated in ASD
ASD-Risk was incorporated with a feature selection algorithm inheritable bi-objective combinatorial genetic algorithm (IBCGA) to select a small set of temporospatial features associated with the risk genes of ASD

Summary

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a repetitive behavior, difficulty in communication, and deceit in social interaction. There is an emerging evidence that demonstrated that heritability is one of the important factors that associated with ASD. Genomic variations such as genetic syndromes, copy number variations, and mutations were observed in approximately 20% of the cases with ASD (Abrahams and Geschwind, 2008; Rosenberg et al, 2009). A twin study reported that environmental liability influences the ASD risk (Nordenbæk et al, 2014). A large population-based study on siblings including monozygotic and dizygotic twins reported that equal contribution of environmental factors and hereditary are the important risk factors of ASD (Sandin et al, 2014). Only about 65 genes out of an estimated several hundred are known to be involved in ASD based on strong genetic evidences (Sanders et al, 2015)

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