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.

Heritability estimates support the contribution of genetics and the environment to the etiology of Autism Spectrum Disorder (ASD), but a role for gene-environment interactions is insufficiently explored. Genes involved in detoxification pathways and physiological permeability barriers (e.g., blood-brain barrier, placenta and respiratory airways), which regulate the effects of exposure to xenobiotics during early stages of neurodevelopment when the immature brain is extremely vulnerable, may be particularly relevant in this context. Our objective was to identify genes involved in the regulation of xenobiotic detoxification or the function of physiological barriers (the XenoReg genes) presenting predicted damaging variants in subjects with ASD, and to understand their interaction patterns with ubiquitous xenobiotics previously implicated in this disorder. We defined a panel of 519 XenoReg genes through literature review and database queries. Large ASD datasets were inspected for in silico predicted damaging Single Nucleotide Variants (SNVs) (N = 2,674 subjects) or Copy Number Variants (CNVs) (N = 3,570 subjects) in XenoReg genes. We queried the Comparative Toxicogenomics Database (CTD) to identify interaction pairs between XenoReg genes and xenobiotics. The interrogation of ASD datasets for variants in the XenoReg gene panel identified 77 genes with high evidence for a role in ASD, according to pre-specified prioritization criteria. These include 47 genes encoding detoxification enzymes and 30 genes encoding proteins involved in physiological barrier function, among which 15 are previous reported candidates for ASD. The CTD query revealed 397 gene-environment interaction pairs between these XenoReg genes and 80% (48/60) of the analyzed xenobiotics. The top interacting genes and xenobiotics were, respectively, CYP1A2, ABCB1, ABCG2, GSTM1, and CYP2D6 and benzo-(a)-pyrene, valproic acid, bisphenol A, particulate matter, methylmercury, and perfluorinated compounds. Individuals carrying predicted damaging variants in high evidence XenoReg genes are likely to have less efficient detoxification systems or impaired physiological barriers. They can therefore be particularly susceptible to early life exposure to ubiquitous xenobiotics, which elicit neuropathological mechanisms in the immature brain, such as epigenetic changes, oxidative stress, neuroinflammation, hypoxic damage, and endocrine disruption. As exposure to environmental factors may be mitigated for individuals with risk variants, this work provides new perspectives to personalized prevention and health management policies for ASD.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with heterogeneous clinical presentation, variable severity, and multiple comorbidities. A complex underlying genetic architecture matches the clinical heterogeneity, and evidence indicates that several co-occurring brain disorders share a genetic component with ASD. In this study, we established a genetic similarity disease network approach to explore the shared genetics between ASD and frequent comorbid brain diseases (and subtypes), namely Intellectual Disability, Attention-Deficit/Hyperactivity Disorder, and Epilepsy, as well as other rarely co-occurring neuropsychiatric conditions in the Schizophrenia and Bipolar Disease spectrum. Using sets of disease-associated genes curated by the DisGeNET database, disease genetic similarity was estimated from the Jaccard coefficient between disease pairs, and the Leiden detection algorithm was used to identify network disease communities and define shared biological pathways. We identified a heterogeneous brain disease community that is genetically more similar to ASD, and that includes Epilepsy, Bipolar Disorder, Attention-Deficit/Hyperactivity Disorder combined type, and some disorders in the Schizophrenia Spectrum. To identify loss-of-function rare de novo variants within shared genes underlying the disease communities, we analyzed a large ASD whole-genome sequencing dataset, showing that ASD shares genes with multiple brain disorders from other, less genetically similar, communities. Some genes (e.g., SHANK3, ASH1L, SCN2A, CHD2, and MECP2) were previously implicated in ASD and these disorders. This approach enabled further clarification of genetic sharing between ASD and brain disorders, with a finer granularity in disease classification and multi-level evidence from DisGeNET. Understanding genetic sharing across disorders has important implications for disease nosology, pathophysiology, and personalized treatment.

Introduction: Autism Spectrum Disorder (ASD) is a clinically heterogeneous neurodevelopmental disorder defined by deficits in social communication and interaction and repetitive and stereotyped interests and behaviors. ASD heritability estimates of 50-83% support a strong role of genetics in its onset, with large sequencing studies reporting a high burden of rare potentially pathogenic copy number variants (CNVs) and single nucleotide variants (SNVs) in affected subjects. Recent data strongly suggests that prenatal to postnatal exposure to ubiquitous environmental factors (e.g. environmental toxins, medications and nutritional factors) contribute to ASD risk. Detoxification processes and physiological permeability barriers (i.e. blood-brain barrier, placenta and respiratory cilia) are crucial in regulating exposure and response to external agents during early development. Thus, the objectives of this study were: 1) to find genes involved in detoxification and regulation of barriers permeability with a high load of relevant CNVs and SNVs in ASD subjects; 2) to explore interactions between the identified genes and environmental factors relevant for the disorder. Material and Methods: Through literature and databases review we searched for genes involved in detoxification and regulation of barriers permeability processes. Genetic data collected from large datasets of subjects with ASD (Autism Genome Project (AGP), Simmons Simplex Collection (SSC), and Autism Sequencing Consortium (ASC)) was used to identify potentially pathogenic variants targeting detoxification and barrier genes. Data from control subjects without neuropsychiatric disorder history was used for comparison purposes. The Comparative Toxicogenomics Database (CTD) was interrogated to identify putatively relevant gene-environment interactions reported in humans throughout the literature. Results:We compiled a list of 519 genes involved in detoxification and regulation of permeability barriers. The analysis of AGP and SSC data resulted in the identification of 7 genes more-frequently targeted by CNVs in ASD subjects from both datasets, after Bonferroni correction for multiple testing (AGP: P<3.5211x10 -4 ; SSC: P< 4.587x10 -4 ). Moreover, 8 genes were exclusively targeted by CNVs from ASD subjects. Regarding SNVs analyses using the ASC dataset, we found 40 genes targeted by potentially pathogenic loss-of-function and/or missense SNVs exclusive to 6 or more cases. The CTD was interrogated for interactions between 55 identified genes and 54 terms for unique chemicals associated with the disorder. A total of 212 gene-environment interaction pairs, between 51/55 (92.7%) genes and 38/54 (70.4%) chemicals, putatively relevant for ASD, were discovered. ABCB1, ABCG2, CYP2C19, GSTM1, CYP2D6, and SLC3A2 were the genes that interacted with more chemicals, while valproic acid, benzo(a)pyrene (b(a)p), bisphenol A, particulate matter and perfluorooctane sulfonic acid (PFOS) were the top chemicals. Discussion: The identified genes code for functionally diverse proteins...