Developing a Predictive Gene Classifier for Autism Spectrum Disorders Based upon Differential Gene Expression Profiles of Phenotypic Subgroups

North American Journal of Medicine & Science
Valerie W Hu, Yinglei Lai

Abstract

Autism spectrum disorders (ASD) are neurodevelopmental disorders which are currently diagnosed solely on the basis of abnormal stereotyped behavior as well as observable deficits in communication and social functioning. Although a variety of candidate genes have been identified on the basis of genetic analyses and up to 20% of ASD cases can be collectively associated with a genetic abnormality, no single gene or genetic variant is applicable to more than 1-2 percent of the general ASD population. In this report, we apply class prediction algorithms to gene expression profiles of lymphoblastoid cell lines (LCL) from several phenotypic subgroups of idiopathic autism defined by cluster analyses of behavioral severity scores on the Autism Diagnostic Interview-Revised diagnostic instrument for ASD. We further demonstrate that individuals from these ASD subgroups can be distinguished from nonautistic controls on the basis of limited sets of differentially expressed genes with a predicted classification accuracy of up to 94% and sensitivities and specificities of ~90% or better, based on support vector machine analyses with leave-one-out validation. Validation of a subset of the "classifier" genes by high-throughput quantitative nucle...Continue Reading

Citations

Mar 10, 2016·Journal of Autism and Developmental Disorders·Hannah CholemkeryChristine M Freitag
Feb 1, 2017·Clinical Psychopharmacology and Neuroscience : the Official Scientific Journal of the Korean College of Neuropsychopharmacology·Dong Hoon OhDong Hyun Ahn
Jul 2, 2021·Frontiers in Genetics·Yenching LinShinn-Ying Ho

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