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

Abstract

The complex genetic architecture of Autism Spectrum Disorder (ASD) and its heterogeneous phenotype makes 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 the 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 behavior 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 (0.82) but low recall (0.39), for a subset of patients with higher biological Information Content scores. This study shows that milder and more severe clinical presentations can have distinct underlying biological mechanisms. It further highlights how machine-learning approaches can reduce clinical heterogeneity by using multidimensional clinical measures, and establishes genotype-phenotype correlations in ASD. However, predictions are strongly dependent on patient's information content. Findings are therefore a first step toward the translation of genetic information into clinically useful applications, and emphasize the need for larger datasets with very complete clinical and biological information.

Automatic Classification of Adult Males With and Without Autism Spectrum Disorder by Non-contact Measurement of Autonomic Nervous System Activation

“…It is now wellacknowledged that people with ASD are a heterogeneous group and the symptomatic profiles differ greatly among patients (51). This observation gained further support from the emerging view that each symptom of ASD derives from a different set of etiological factors (52,53). Given this, it is highly possible that participants in our ASD group had heterogeneous characteristics, which might have yielded a variance in ANS responses to emotional clips.…”

Identifying Subgroups of Patients With Autism by Gene Expression Profiles Using Machine Learning Algorithms

“…To tackle these challenges, one of the solutions is to implement state-of-the-art statistical methods that can efficiently parse through high-dimensionality data, such as machine learning (ML) algorithms, to differentiate subgroups with meaningful etiological, diagnostic, or therapeutic implications ( 18 ). Previous evidence suggests that ML algorithms can be used to reduce the number of items from standardized ASD assessment tools to make the assessment more efficient ( 19 ) and predict clinical outcomes with ASD phenotypic clusters and genetic data of copy number variations ( 20 ). The ML algorithms appear to be useful to identify phenotypic clusters as ASD subgroups that can predict clinical outcomes ( 21 ).…”

Exposure to Xenobiotics and Gene-Environment Interactions in Autism Spectrum Disorder: A Systematic Review

“…These CNVs and SNVs map to dozens of different candidate genes, which frequently cluster in neurobiological pathways (e.g. synaptic processes, behavior regulation, cognition and neuronal signaling) as well as in chromatin modification and gene expression regulation processes [4][5][6][7], providing evidence for the biological mechanisms disrupted in the disorder.…”