Autistic Adult Services Availability, Preferences, and User Experiences : Results From the Autism Spectrum Disorder in the European Union Survey

There is very little knowledge regarding autistic adult services, practices, and delivery. The study objective was to improve understanding of current services and practices for autistic adults and opportunities for improvement as part of the Autism Spectrum Disorder in the European Union (ASDEU) project. Separate survey versions were created for autistic adults, carers of autistic adults, and professionals in adult services. 2,009 persons responded to the survey and 1,085 (54%) of them completed at least one of the services sections: 469 autistic adults (65% female; 55% 50% responded "don't know"). Five of seven residential services features recommended for autistic adults were experienced byPeer reviewe

Maternal CNV transmission to sons with autism correlates with phenotypic traits in the Broad Autism Phenotype

Autism Spectrum Disorder (ASD): Common neurodevelopmental disorder, global prevalence ~1 %; Persistent deficits in social communication and social interaction; restricted and repetitive behavior, interests, or activities – highly heterogeneous clinical presentation; Male to female ratio ~4:1

Translating the complex ASD genetic architecture into clinical phenotype using an integrative system biology approach

Objective: The global objective of this study is to improve ASD diagnosis and prognosis by dissecting the complex genotype-phenotype associations using an integrative systems biology approach.MA is supported by the Fundação para a Ciência e Tecnologia, Portugal (SFRH/BD/52485/2014). Patients and parents were genotyped in the context of the Autism Genome Project (AGP), funded by NIMH, HRB, MRC, Autism Speaks, Hilibrand Foundation, Genome Canada, OGI, CIHR.N/

An integrative system biology approach to delineate complex genotype-phenotype associations in Autism Spectrum Disorder

Objectives: The global objective of the study is to improve the current knowledge on ASD prognosis and diagnosis by delineating the complex genotype-phenotype associations using an integrative systems biology approach. For this purpose three specific objectives were pursued: - To identify clinically similar subgroups of individuals with ASD; - To find biological processes disrupted by rare CNVs targeting brain genes in ASD subjects; - To train a machine learning classifier for the clinical prediction of disease progression from genetic information in very young children.MA is supported by the Fundação para a Ciência e Tecnologia, Portugal (SFRH/BD/52485/2014). Patients and parents were genotyped in the context of the Autism Genome Project (AGP), funded by NIMH, HRB, MRC, Autism Speaks, Hilibrand Foundation, Genome Canada, OGI, CIHR.N/

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

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.The work was supported by Portuguese Fundação para a Ciência e Tecnologia (FCT) through funding to BioISI (Ref: UID/MULTI/04046/2013), LASIGE Research Unit (Ref: UID/CEC/00408/2019), and to DeST: Deep Semantic Tagger project (Ref: PTDC/CCI-BIO/28685/2017). M.A., A.R.M., J.X.S., and J.V. were the recipients of BioSys PhD programme fellowship from FCT (Portugal) with references PD/BD/52485/2014, PD/BD/113773/2015, PD/BD/114386/2016, and PD/BD/\131390/2017, respectively. C.R. is the recipient of a grant from FCT (Ref: POCI01-0145-FEDER-016428). Patients and parents were genotyped in the context of the Autism Genome Project (AGP), funded by NIMH, HRB, MRC, Autism Speaks, Hilibrand Foundation, Genome Canada, OGI, and CIHR. We acknowledge the families who participated in these projects.info:eu-repo/semantics/publishedVersio

Determinants of satisfaction with the detection process of autism in Europe: Results from the ASDEU study

International audienc