DOI: 10.1101/470757Nov 16, 2018Paper

Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learning

BioRxiv : the Preprint Server for Biology
Muhammad AsifAstrid M. Vicente

Abstract

The complex genetic architecture of Autism Spectrum Disorder (ASD) and its heterogeneous phenotype make molecular diagnosis and patient prognosis challenging tasks. To establish more precise genotype-phenotype correlations in ASD, we developed a novel machine learning integrative approach, which seeks to delineate associations between patients clinical profiles and disrupted biological processes inferred from their Copy Number Variants (CNVs) that span brain genes. Clustering analysis of relevant clinical measures from 2446 ASD cases in the Autism Genome Project identified two distinct phenotypic subgroups. Patients in these clusters differed significantly in ADOS-defined severity, adaptive behaviour profiles, intellectual ability and verbal status, the latter contributing the most for cluster stability and cohesion. Functional enrichment analysis of brain genes disrupted by CNVs in these ASD cases identified 15 statistically significant biological processes, including cell adhesion, neural development, cognition and polyubiquitination, in line with previous ASD findings. A Naive Bayes classifier, generated to predict the ASD phenotypic clusters from disrupted biological processes, achieved predictions with a high Precision (0....Continue Reading

Related Concepts

Autistic Disorder
Brain
Cell Adhesion
Classification
Statistical Cluster
Cognition
Enzyme Stability
Gene Clusters
Genome
Learning

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