Behavioral signatures related to genetic disorders in autism

DeepASDPred: a CNN-LSTM-based deep learning method for Autism spectrum disorders risk RNA identification

BACKGROUND

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterized by difficulty communicating with society and others, behavioral difficulties, and a brain that processes information differently than normal. Genetics has a strong impact on ASD associated with early onset and distinctive signs. Currently, all known ASD risk genes are able to encode proteins, and some de novo mutations disrupting protein-coding genes have been demonstrated to cause ASD. Next-generation sequencing technology enables high-throughput identification of ASD risk RNAs. However, these efforts are time-consuming and expensive, so an efficient computational model for ASD risk gene prediction is necessary.

RESULTS

In this study, we propose DeepASDPerd, a predictor for ASD risk RNA based on deep learning. Firstly, we use K-mer to feature encode the RNA transcript sequences, and then fuse them with corresponding gene expression values to construct a feature matrix. After combining chi-square test and logistic regression to select the best feature subset, we input them into a binary classification prediction model constructed by convolutional neural network and long short-term memory for training and classification. The results of the tenfold cross-validation proved our method outperformed the state-of-the-art methods. Dataset and source code are available at https://github.com/Onebear-X/DeepASDPred is freely available.

CONCLUSIONS

Our experimental results show that DeepASDPred has outstanding performance in identifying ASD risk RNA genes.

Phenomic Studies on Diseases: Potential and Challenges

The rapid development of such research field as multi-omics and artificial intelligence (AI) has made it possible to acquire and analyze the multi-dimensional big data of human phenomes. Increasing evidence has indicated that phenomics can provide a revolutionary strategy and approach for discovering new risk factors, diagnostic biomarkers and precision therapies of diseases, which holds profound advantages over conventional approaches for realizing precision medicine: first, the big data of patients' phenomes can provide remarkably richer information than that of the genomes; second, phenomic studies on diseases may expose the correlations among cross-scale and multi-dimensional phenomic parameters as well as the mechanisms underlying the correlations; and third, phenomics-based studies are big data-driven studies, which can significantly enhance the possibility and efficiency for generating novel discoveries. However, phenomic studies on human diseases are still in early developmental stage, which are facing multiple major challenges and tasks: first, there is significant deficiency in analytical and modeling approaches for analyzing the multi-dimensional data of human phenomes; second, it is crucial to establish universal standards for acquirement and management of phenomic data of patients; third, new methods and devices for acquirement of phenomic data of patients under clinical settings should be developed; fourth, it is of significance to establish the regulatory and ethical guidelines for phenomic studies on diseases; and fifth, it is important to develop effective international cooperation. It is expected that phenomic studies on diseases would profoundly and comprehensively enhance our capacity in prevention, diagnosis and treatment of diseases.

Heterogeneity of Autism Characteristics in Genetic Syndromes: Key Considerations for Assessment and Support

Purpose of Review

Elevated prevalence of autism characteristics is reported in genetic syndromes associated with intellectual disability. This review summarises recent evidence on the behavioural heterogeneity of autism in the following syndromes: Fragile X, Cornelia de Lange, Williams, Prader-Willi, Angelman, Down, Smith-Magenis, and tuberous sclerosis complex. Key considerations for assessment and support are discussed.

Recent Findings

The profile and developmental trajectory of autism-related behaviour in these syndromes indicate some degree of syndrome specificity which may interact with broader behavioural phenotypes (e.g. hypersociability), intellectual disability, and mental health (e.g. anxiety). Genetic subtype and co-occurring epilepsy within syndromes contribute to increased significance of autism characteristics. Autism-related strengths and challenges are likely to be overlooked or misunderstood using existing screening/diagnostic tools and criteria, which lack sensitivity and specificity within these populations.

Summary

Autism characteristics are highly heterogeneous across genetic syndromes and often distinguishable from non-syndromic autism. Autism diagnostic assessment practices in this population should be tailored to specific syndromes. Service provisions must begin to prioritise needs-led support.

Profiles of autism characteristics in thirteen genetic syndromes: a machine learning approach

BACKGROUND

Phenotypic studies have identified distinct patterns of autistic characteristics in genetic syndromes associated with intellectual disability (ID), leading to diagnostic uncertainty and compromised access to autism-related support. Previous research has tended to include small samples and diverse measures, which limits the generalisability of findings. In this study, we generated detailed profiles of autistic characteristics in a large sample of > 1500 individuals with rare genetic syndromes.

METHODS

Profiles of autistic characteristics based on the Social Communication Questionnaire (SCQ) scores were generated for thirteen genetic syndrome groups (Angelman n = 154, Cri du Chat n = 75, Cornelia de Lange n = 199, fragile X n = 297, Prader-Willi n = 278, Lowe n = 89, Smith-Magenis n = 54, Down n = 135, Sotos n = 40, Rubinstein-Taybi n = 102, 1p36 deletion n = 41, tuberous sclerosis complex n = 83 and Phelan-McDermid n = 35 syndromes). It was hypothesised that each syndrome group would evidence a degree of specificity in autistic characteristics. To test this hypothesis, a classification algorithm via support vector machine (SVM) learning was applied to scores from over 1500 individuals diagnosed with one of the thirteen genetic syndromes and autistic individuals who did not have a known genetic syndrome (ASD; n = 254). Self-help skills were included as an additional predictor.

RESULTS

Genetic syndromes were associated with different but overlapping autism-related profiles, indicated by the substantial accuracy of the entire, multiclass SVM model (55% correctly classified individuals). Syndrome groups such as Angelman, fragile X, Prader-Willi, Rubinstein-Taybi and Cornelia de Lange showed greater phenotypic specificity than groups such as Cri du Chat, Lowe, Smith-Magenis, tuberous sclerosis complex, Sotos and Phelan-McDermid. The inclusion of the ASD reference group and self-help skills did not change the model accuracy.

LIMITATIONS

The key limitations of our study include a cross-sectional design, reliance on a screening tool which focuses primarily on social communication skills and imbalanced sample size across syndrome groups.

CONCLUSIONS

These findings replicate and extend previous work, demonstrating syndrome-specific profiles of autistic characteristics in people with genetic syndromes compared to autistic individuals without a genetic syndrome. This work calls for greater precision of assessment of autistic characteristics in individuals with genetic syndromes associated with ID.

Improvement of the Clinical and Psychological Profile of Patients with Autism after Methylcobalamin Syrup Administration

(1) Background: Autism, also known as autism-spectrum disorder, is a pervasive developmental disorder affecting social skills and psychological status in particular. The complex etiopathogenesis of autism limits efficient therapy, which leads to problems with the normal social integration of the individual and causes severe family distress. Injectable methylcobalamin was shown to improve the clinical status of patients via enhanced cell oxidative status and/or methylation capacity. Here we tested the efficiency of a syrup form of methylcobalamin in treating autism. (2) Methods: Methylcobalamin was administered daily at 500 µg dose to autistic children and young adults (n = 25) during a 200-day period. Clinical and psychological status was evaluated by parents and psychologists and plasma levels of reduced and oxidized glutathione, vitamin B12, homocysteine, and cysteine were determined before the treatment, and at day 100 and day 200 of the treatment. (3) Results: Good patient compliance was reported. Methylcobalamin treatment gradually improved the overall clinical and psychological status, with the highest impact in the social domain, followed by the cognitive, behavioral and communication characteristics. Changes in the clinical and psychological status were strongly associated with the changes in the level of reduced glutathione and reduced/oxidized glutathione ratio. (4) Conclusion: A high dose of methylcobalamin administered in syrup form ameliorates the clinical and psychological status of autistic individuals, probably due to the improved oxidative status.

Genes To Mental Health (G2MH): A Framework to Map the Combined Effects of Rare and Common Variants on Dimensions of Cognition and Psychopathology

Rare genomic disorders (RGDs) confer elevated risk for neurodevelopmental psychiatric disorders. In this era of intense genomics discoveries, the landscape of RGDs is rapidly evolving. However, there has not been comparable progress to date in scalable, harmonized phenotyping methods. As a result, beyond associations with categorical diagnoses, the effects on dimensional traits remain unclear for many RGDs. The nature and specificity of RGD effects on cognitive and behavioral traits is an area of intense investigation: RGDs are frequently associated with more than one psychiatric condition, and those studied to date affect, to varying degrees, a broad range of developmental and cognitive functions. Although many RGDs have large effects, phenotypic expression is typically influenced by additional genomic and environmental factors. There is emerging evidence that using polygenic risk scores in individuals with RGDs offers opportunities to refine prediction, thus allowing for the identification of those at greatest risk of psychiatric illness. However, translation into the clinic is hindered by roadblocks, which include limited genetic testing in clinical psychiatry, and the lack of guidelines for following individuals with RGDs, who are at high risk of developing psychiatric symptoms. The Genes to Mental Health Network (G2MH) is a newly funded National Institute of Mental Health initiative that will collect, share, and analyze large-scale data sets combining genomics and dimensional measures of psychopathology spanning diverse populations and geography. The authors present here the most recent understanding of the effects of RGDs on dimensional behavioral traits and risk for psychiatric conditions and discuss strategies that will be pursued within the G2MH network, as well as how expected results can be translated into clinical practice to improve patient outcomes.

Patterns of psychopathology and cognition in sex chromosome aneuploidy

Background

Sex chromosome aneuploidies (SCAs) are a collectively common family of genetic disorders that increase the risk for neuropsychiatric and cognitive impairment. Beyond being important medical disorders in their own right, SCAs also offer a unique naturally occurring model for studying X- and Y-chromosome influences on the human brain. However, it remains unclear if (i) different SCAs are associated with different profiles of psychopathology and (ii) the notable interindividual variation in psychopathology is related to co-occurring variation in cognitive ability.

Methods

We examined scores for 11 dimensions of psychopathology [Child/Adult Behavior Checklist (CBCL)] and general cognitive ability [full-scale IQ (FSIQ) from Wechsler tests] in 110 youth with varying SCAs (XXY = 41, XYY = 22, XXX = 27, XXYY = 20) and 131 typically developing controls (XX = 59, XY = 72).

Results

All SCAs were associated with elevated CBCL scores across several dimensions of psychopathology (two-sample t tests comparing the euploidic and aneuploidic groups [all |T| > 9, and p < 0.001]). Social and attentional functioning were particularly sensitive to the carriage of a supernumerary Y-chromosome. In particular, the XYY group evidenced significantly more social problems than both extra-X groups (Cohen’s d effect size > 0.5, Bonferroni corrected p < .05). There was marked variability in CBCL scores within each SCA group, which generally correlated negatively with IQ, but most strongly so for social and attentional difficulties (standardized β, − 0.3). These correlations showed subtle differences as a function of the SCA group and CBCL scale.

Conclusions

There is domain-specific variation in psychopathology across SCA groups and domain-specific correlation between psychopathology and IQ within SCAs. These findings (i) help to tailor clinical assessment of this common and impactful family of genetic disorders and (ii) suggest that dosage abnormalities of X- and Y-linked genes impart somewhat distinct profiles of neuropsychiatric risk.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-021-09407-9.

Harnessing rare variants in neuropsychiatric and neurodevelopment disorders-a Keystone Symposia report

Neurodevelopmental neuropsychiatric disorders, such as autism spectrum disorder and schizophrenia, have strong genetic risk components, but the underlying mechanisms have proven difficult to decipher. Rare, high-risk variants may offer an opportunity to delineate the biological mechanisms responsible more clearly for more common idiopathic diseases. Indeed, different rare variants can cause the same behavioral phenotype, demonstrating genetic heterogeneity, while the same rare variant can cause different behavioral phenotypes, demonstrating variable expressivity. These observations suggest convergent underlying biological and neurological mechanisms; identification of these mechanisms may ultimately reveal new therapeutic targets. At the 2021 Keystone eSymposium "Neuropsychiatric and Neurodevelopmental Disorders: Harnessing Rare Variants" a panel of experts in the field described significant progress in genomic discovery and human phenotyping and raised several consistent issues, including the need for detailed natural history studies of rare disorders, the challenges in cohort recruitment, and the importance of viewing phenotypes as quantitative traits that are impacted by rare variants.

Profile of Autism Spectrum Disorder in Tuberous Sclerosis Complex: Results from a Longitudinal, Prospective, Multisite Study

OBJECTIVE

Tuberous sclerosis complex (TSC) is highly associated with autism spectrum disorder (ASD). Objectives of the study were to characterize autistic features in young children with TSC.

METHODS

Participants included 138 children followed from ages 3 to 36 months with TSC from the Tuberous Sclerosis Complex Autism Center of Excellence Research Network (TACERN), a multicenter, prospective observational study aimed at understanding the underlying mechanisms of ASD in TSC. Developmental and autism-specific assessments were administered, and a clinical diagnosis of ASD was determined for all participants at 36 months. Further analyses were performed on 117 participants with valid autism assessments based on nonverbal mental age greater than 15 months.

RESULTS

Prevalence of clinical diagnosis of ASD at 36 months was 25%. Nearly all autistic behaviors on the Autism Diagnostic Observation Schedule-2 (ADOS-2) and Autism Diagnostic Interview-Revised (ADI-R) were more prevalent in children diagnosed with ASD; however, autism-specific behaviors were also observed in children without ASD. Overall quality of social overtures, facial expressions, and abnormal repetitive interests and behaviors were characteristics most likely to distinguish children with ASD from those without an ASD diagnosis. Participants meeting ADOS-2 criteria but not a clinical ASD diagnosis exhibited intermediate developmental and ADOS-2 scores compared to individuals with and without ASD.

INTERPRETATION

ASD is highly prevalent in TSC, and many additional individuals with TSC exhibit a broad range of subthreshold autistic behaviors. Our findings reveal a broader autism phenotype that can be identified in young children with TSC, which provides opportunity for early targeted treatments. ANN NEUROL 2021.

The Connectivity Fingerprint of the Fusiform Gyrus Captures the Risk of Developing Autism in Infants with Tuberous Sclerosis Complex

Tuberous sclerosis complex (TSC) is a rare genetic disorder characterized by benign tumors throughout the body; it is generally diagnosed early in life and has a high prevalence of autism spectrum disorder (ASD), making it uniquely valuable in studying the early development of autism, before neuropsychiatric symptoms become apparent. One well-documented deficit in ASD is an impairment in face processing. In this work, we assessed whether anatomical connectivity patterns of the fusiform gyrus, a central structure in face processing, capture the risk of developing autism early in life. We longitudinally imaged TSC patients at 1, 2, and 3 years of age with diffusion compartment imaging. We evaluated whether the anatomical connectivity fingerprint of the fusiform gyrus was associated with the risk of developing autism measured by the Autism Observation Scale for Infants (AOSI). Our findings suggest that the fusiform gyrus connectivity captures the risk of developing autism as early as 1 year of age and provides evidence that abnormal fusiform gyrus connectivity increases with age. Moreover, the identified connections that best capture the risk of developing autism involved the fusiform gyrus and limbic and paralimbic regions that were consistent with the ASD phenotype, involving an increased number of left-lateralized structures with increasing age.

Genetic Influences on Disease Subtypes

Disease classification, or nosology, was historically driven by careful examination of clinical features of patients. As technologies to measure and understand human phenotypes advanced, so too did classifications of disease, and the advent of genetic data has led to a surge in genetic subtyping in the past decades. Although the fundamental process of refining disease definitions and subtypes is shared across diverse fields, each field is driven by its own goals and technological expertise, leading to inconsistent and conflicting definitions of disease subtypes. Here, we review several classical and recent subtypes and subtyping approaches and provide concrete definitions to delineate subtypes. In particular, we focus on subtypes with distinct causal disease biology, which are of primary interest to scientists, and subtypes with pragmatic medical benefits, which are of primary interest to physicians. We propose genetic heterogeneity as a gold standard for establishing biologically distinct subtypes of complex polygenic disease. We focus especially on methods to find and validate genetic subtypes, emphasizing common pitfalls and how to avoid them.

A cross-comparison of cognitive ability across 8 genomic disorders

Genomic disorders result from rearrangement of the human genome. Most genomic disorders are caused by copy number variants (CNV), deletions or duplications of several hundred kilobases. Many CNV loci are associated with autism, schizophrenia, and most commonly, intellectual disability (ID). However, there is little comparison of cognitive ability measures across these CNV disorders. This study aims to understand whether existing data can be leveraged for a cross-comparison of cognitive ability among multiple CNV. We found there is a lack of harmonization among assessment instruments and little standardization for reporting summary data across studies. Despite these limitations, we identified a differential impact of CNV loci on cognitive ability. Our data suggest that future cross-comparisons of CNV disorders will reveal meaningful differences across the phenotypic spectrum, especially if standardized phenotypic assessment is achieved.

Identification of autism spectrum disorder based on short-term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network

OBJECTIVE

To classify children with autism spectrum disorder (ASD) and typical development (TD) using short-term spontaneous hemodynamic fluctuations and to explore the abnormality of inferior frontal gyrus and temporal lobe in ASD.

METHODS

25 ASD children and 22 TD children were measured with functional near-infrared spectroscopy located on the inferior frontal gyrus and temporal lobe. To extract features used to classify ASD and TD, a multi-layer neural network was applied, combining with a three-layer convolutional neural network, a layer of long and short-term memory network (LSTM) and a layer of LSTM with Attention mechanism. In order to shorten the time of data collection and get more information from limited samples, a sliding window with 3.5 s width was utilized after comparisons, and numerous short (3.5 s) fNIRS time series were then obtained and used as the input of the multi-layer neural network.

RESULTS

A good classification between ASD and TD was obtained with considerably high accuracy by using a multi-layer neural network in different brain regions, especially in the left temporal lobe, where sensitivity of 90.6% and specificity of 97.5% achieved.

CONCLUSIONS

The "CLAttention" multi-layer neural network has the potential to excavate more meaningful features to distinguish between ASD and TD. Moreover, the temporal lobe may be worth further study.

SIGNIFICANCE

The findings in this study may have implications for rapid diagnosis of children with ASD and provide a new perspective for future medical diagnosis.

A Genetics-First Approach to Dissecting the Heterogeneity of Autism: Phenotypic Comparison of Autism Risk Copy Number Variants

OBJECTIVE

Certain copy number variants (CNVs) greatly increase the risk of autism. The authors conducted a genetics-first study to investigate whether heterogeneity in the clinical presentation of autism is underpinned by specific genotype-phenotype relationships.

METHODS

This international study included 547 individuals (mean age, 12.3 years [SD=4.2], 54% male) who were ascertained on the basis of having a genetic diagnosis of a rare CNV associated with high risk of autism (82 16p11.2 deletion carriers, 50 16p11.2 duplication carriers, 370 22q11.2 deletion carriers, and 45 22q11.2 duplication carriers), as well as 2,027 individuals (mean age, 9.1 years [SD=4.9], 86% male) with autism of heterogeneous etiology. Assessments included the Autism Diagnostic Interview-Revised and IQ testing.

RESULTS

The four genetic variant groups differed in autism symptom severity, autism subdomain profile, and IQ profile. However, substantial variability was observed in phenotypic outcome in individual genetic variant groups (74%-97% of the variance, depending on the trait), whereas variability between groups was low (1%-21%, depending on the trait). CNV carriers who met autism criteria were compared with individuals with heterogeneous autism, and a range of profile differences were identified. When clinical cutoff scores were applied, 54% of individuals with one of the four CNVs who did not meet full autism diagnostic criteria had elevated levels of autistic traits.

CONCLUSIONS

Many CNV carriers do not meet full diagnostic criteria for autism but nevertheless meet clinical cutoffs for autistic traits. Although profile differences between variants were observed, there is considerable variability in clinical symptoms in the same variant.

Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features

Background

Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the currently known ASD risk genes code for proteins, long non-coding RNAs (lncRNAs) as essential regulators of gene expression have been implicated in ASD. Some lncRNAs show altered expression levels in autistic brains, but their roles in ASD pathogenesis are still unclear.

Results

In this study, we have developed a new machine learning approach to predict candidate lncRNAs associated with ASD. Particularly, the knowledge learnt from protein-coding ASD risk genes was transferred to the prediction and prioritization of ASD-associated lncRNAs. Both developmental brain gene expression data and transcript sequence were found to contain relevant information for ASD risk gene prediction. During the pre-training phase of model construction, an autoencoder network was implemented for a representation learning of the gene expression data, and a random-forest-based feature selection was applied to the transcript-sequence-derived k-mers. Our models, including logistic regression, support vector machine and random forest, showed robust performance based on tenfold cross-validations as well as candidate prioritization with hypothetical loci. We then utilized the models to predict and prioritize a list of candidate lncRNAs, including some reported to be cis-regulators of known ASD risk genes, for further investigation.

Conclusions

Our results suggest that ASD risk genes can be accurately predicted using developmental brain gene expression data and transcript sequence features, and the models may provide useful information for functional characterization of the candidate lncRNAs associated with ASD.

An Overview of Bioinformatics Methods for Analyzing Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders and are well recognized to be biologically heterogeneous in which various factors are associated, including genetic, metabolic, and environmental ones. Despite its high prevalence, only a few drugs have been approved for the treatment of ASD. Therefore, extensive studies have been conducted to identify ASD risk genes and novel drug targets. Since many genes and many other factors are associated with ASD, various bioinformatics methods have also been developed for the analysis of ASD. In this paper, we review bioinformatics methods for analyzing ASD data with the focus on computational aspects. We classify existing methods into two categories: (i) methods based on genomic variants and gene expression data, and (ii) methods using biological networks, which include gene co-expression networks and protein-protein interaction networks. Next, for each method, we provide an overall flow and elaborate on the computational techniques used. We also briefly review other approaches and discuss possible future directions and strategies for developing bioinformatics approaches to analyze ASD.

Reciprocal Copy Number Variations at 22q11.2 Produce Distinct and Convergent Neurobehavioral Impairments Relevant for Schizophrenia and Autism Spectrum Disorder

BACKGROUND

22q11.2 deletions and duplications are copy number variations (CNVs) that predispose to developmental neuropsychiatric disorders. Both CNVs are associated with autism spectrum disorder (ASD), while the deletion confers disproportionate risk for schizophrenia. Neurobehavioral profiles associated with these reciprocal CNVs in conjunction with brain imaging measures have not been reported.

METHODS

We profiled the impact of 22q11.2 CNVs on neurobehavioral measures relevant to ASD and psychosis in 106 22q11.2 deletion carriers, 38 22q11.2 duplication carriers, and 82 demographically matched healthy control subjects. To determine whether brain-behavior relationships were altered in CNV carriers, we further tested for interactions between group and regional brain structure on neurobehavioral domains.

RESULTS

Cognitive deficits were observed in both CNV groups, with the lowest IQs in deletion carriers. ASD and dimensionally measured ASD traits were elevated in both CNV groups; however, duplication carriers exhibited increased stereotypies compared to deletion carriers. Moreover, discriminant analysis using ASD subdomains distinguished between CNV cases with 76% accuracy. Both psychotic disorder diagnosis and dimensionally measured positive and negative symptoms were elevated in deletion carriers. Finally, healthy control subjects showed an inverse relationship between processing speed and cortical thickness in heteromodal association areas, which was absent in both CNV groups.

CONCLUSIONS

22q11.2 CNVs differentially modulate intellectual functioning and psychosis-related symptomatology but converge on broad ASD-related symptomatology. However, subtle differences in ASD profiles distinguish CNV groups. Processing speed impairments, coupled with the lack of normative relationship between processing speed and cortical thickness in CNV carriers, implicate aberrant development of the cortical mantle in the pathology underlying impaired processing speed ability.

Modeling Neurodevelopmental Deficits in Tuberous Sclerosis Complex with Stem Cell Derived Neural Precursors and Neurons

Tuberous sclerosis complex (TSC) is a rare genetic disorder that is caused by mutations in TSC1 or TSC2. TSC is a multi-organ disorder characterized by development of non-malignant cellular overgrowths, called hamartomas, in different organs of the body. TSC is also characterized as a neurodevelopmental disorder presenting with epilepsy and autism, and formation of cortical malformations ("tubers"), subependymal giant cell astrocytomas (SEGAs), and subependymal nodules (SENs) in the patient's brain. In this chapter, we are going to give an overview of neural stem cell and neuronal development in TSC. In addition, we will also describe previously developed animal models of TSC that display seizures, autistic-like behaviors, and neuronal cell abnormalities in vivo, and we will compare them to disease phenotypes detected with human stem cell derived neuronal cells in vitro. We will describe the effects of TSC-mutations in different neural cell subtypes, and discuss the mitochondrial function, autophagy, and synaptic development and functional deficits in the neurons. Finally, we will review utilization of these human TSC-patient derived neuronal models for drug screening to develop new treatment options for the neurological phenotypes seen in TSC patients.

Neuroanatomical underpinnings of autism symptomatology in carriers and non-carriers of the 22q11.2 microdeletion

BACKGROUND

A crucial step to understanding the mechanistic underpinnings of autism spectrum disorder (ASD), is to examine if the biological underpinnings of ASD in genetic high-risk conditions, like 22q11.2 deletion syndrome (22q11.2DS), are similar to those in idiopathic illness. This study aimed to examine if ASD symptomatology in 22q11.2DS is underpinned by the same-or distinct-neural systems that mediate these symptoms in non-deletion carriers.

METHODS

We examined vertex-wise estimates of cortical volume (CV), surface area (SA), and cortical thickness across 131 individuals between 6 and 25 years of age including (1) 50 individuals with 22q11.2DS, out of which n = 25 had a diagnosis of ASD, (2) 40 non-carriers of the microdeletion with a diagnosis of ASD (i.e., idiopathic ASD), and (3) 41 typically developing (TD) controls. We employed a 2-by-2 factorial design to identify neuroanatomical variability associated with the main effects of 22q11.2DS and ASD, as well as their interaction. Further, using canonical correlation analysis (CCA), we compared neuroanatomical variability associated with the complex (i.e., multivariate) clinical phenotype of ASD between 22q11.2 deletion carriers and non-carriers.

RESULTS

The set of brain regions associated with the main effect of 22q11.2DS was distinct from the neuroanatomical underpinnings of the main effect of ASD. Moreover, significant 22q11.2DS-by-ASD interactions were observed for CV and SA in the dorsolateral prefrontal cortex, precentral gyrus, and posterior cingulate cortex, suggesting that the neuroanatomy of ASD is significantly modulated by 22q11.2DS (p < 0.01). We further established that the multivariate patterns of neuroanatomical variability associated with differences in symptom profiles significantly differed between 22q11.2 deletion carriers and non-carriers.

LIMITATIONS

We employed a multicenter design to overcome single-site recruitment limitations; however, FreeSurfer-derived measures of surface anatomy have been shown to be highly reliable across scanner platforms and field strengths. Further, we controlled for gender to address the differing distribution between idiopathic ASD individuals and the other groups. Nonetheless, the gender distribution in our sample reflects that of the respective populations, adding to the generalizability of our results. Last, we included individuals with a relatively wide age range (i.e., 6-25 years).

CONCLUSIONS

Our findings indicate that neuroanatomical correlates of ASD symptomatology in carriers of the 22q11.2 microdeletion diverge from those in idiopathic ASD.

Autism Spectrum Disorder Symptom Profile Across the RASopathies

Dysregulation of the Ras MAPK signaling pathway is implicated in the pathogenesis of autism spectrum disorder (ASD). The RASopathies, a group of disorders caused by mutations of the Ras/MAPK pathway genes, share many overlapping clinical features. Studies suggest a high prevalence of ASD in the RASopathies, but detailed characterization of the ASD profile is lacking. The aim of this study was to compare the ASD symptom profile of three distinct RASopathies associated with both gain-of-function and loss-of-function mutations: neurofibromatosis type 1 (NF1), Noonan syndrome (NS), and cardiofaciocutaneous syndrome (CFC). Participants were drawn from existing databases if they had a diagnosis of a RASopathy, met the criteria for ASD, and were able to communicate verbally. We compared the phenotypic profile of NF1 + ASD (n = 48), NS + ASD (n = 11), and CFC + ASD (n = 7) on the Autism Diagnostic Inventory (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS). We found subtle but non-significant group differences with higher levels of social impairments and lower restricted repetitive behaviors in the NF1 group as compared with the NS and CFC groups. We observed group differences in developmental milestones with most severe delays in CFC, followed by NS and NF1. Our results suggest that despite developmental differences, the ASD profile remains relatively consistent across the three RASopathies. Though our results need confirmation in larger samples, they suggest the possibility that treatment and mechanistic insights developed in the context of one RASopathy may be generalizable to others and possibly to non-syndromic ASD associated with dysregulation of Ras/MAPK pathway genes.

Corpus Callosum White Matter Diffusivity Reflects Cumulative Neurological Comorbidity in Tuberous Sclerosis Complex

INTRODUCTION

Neurological manifestations in Tuberous Sclerosis Complex (TSC) are highly variable. Diffusion tensor imaging (DTI) may reflect the neurological disease burden. We analyzed the association of autism spectrum disorder (ASD), intellectual disability (ID) and epilepsy with callosal DTI metrics in subjects with and without TSC.

METHODS

186 children underwent 3T MRI DTI: 51 with TSC (19 with concurrent ASD), 46 with non-syndromic ASD and 89 healthy controls (HC). Subgroups were based on presence of TSC, ASD, ID, and epilepsy. Density-weighted DTI metrics obtained from tractography of the corpus callosum were fitted using a 2-parameter growth model. We estimated distributions using bootstrapping and calculated half-life and asymptote of the fitted curves.

RESULTS

TSC was associated with a lower callosal fractional anisotropy (FA) than ASD, and ASD with a lower FA than HC. ID, epilepsy and ASD diagnosis were each associated with lower FA values, demonstrating additive effects. In TSC, the largest change in FA was related to a comorbid diagnosis of ASD. Mean diffusivity (MD) showed an inverse relationship to FA. Some subgroups were too small for reliable data fitting.

CONCLUSIONS

Using a cross-disorder approach, this study demonstrates cumulative abnormality of callosal white matter diffusion with increasing neurological comorbidity.

Behavioral characteristics of autism spectrum disorder in very preterm birth children

Background

Lower gestational age may increase autism spectrum disorder (ASD) vulnerability; however, the incidence of ASD diagnosis through a direct assessment on every very preterm birth child on the population base remains unclear. Moreover, the behavioral characteristics of preterm birth ASD are unknown.

Methods

Every very preterm birth child (gestational age < 32 weeks; birth weight < 1500 g) who was discharged from neonatal intensive care units in Southern Taiwan and prospectively followed to 5 years of age was evaluated using the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R). The term birth (gestational age > 37 weeks) ASD children characterized by ADOS and ADI-R were group matched to the preterm birth ASD by age at examination for comparison. ADOS severity scores were calculated by the Mann-Whitney U test and ADI-R by multivariate analysis of variance and canonical discriminant analysis.

Results

Two hundred forty-six (87%) of the 283 very preterm survivors were followed prospectively to 5 years of age. Nineteen (7.7%) of the 246 children fulfilled the diagnostic criteria of ASD. After excluding 1 patient with cerebral palsy and profound mental disability, 18 preterm ASD children were compared with 44 term birth ASD children. The two ASD groups were comparable for age at examination, gender, and intelligence quotient. The two groups showed comparable ADOS severity scores in social affect deficits, restricted repetitive behaviors, and total score, but had differences in qualitative abnormalities in reciprocal social interaction (Wilks lambda F value = 6.2, P < 0.001) of ADI-R. Compared to term birth ASD children, preterm birth ASD children exhibited worse nonverbal behaviors that regulate social interaction (OR 2.59, 95% CI 1.41-4.73, P = 0.002) but more favorable peer relationships (OR 0.58, 95% CI 0.38-0.90, P = 0.01) and socioemotional reciprocity (OR 0.55, 95% CI 0.33-0.92, P = 0.02). In contrast to the heterogeneous severity of social reciprocity in the term ASD group, the behavioral characteristics of the preterm ASD group showed a homogeneous reciprocal social interaction pattern.

Conclusions

The 5-year incidence rate of ASD was high in very preterm birth children. Preterm birth ASD exhibited a specific behavioral phenotype of reciprocal social interaction.

Neuropsychiatric phenotypes and a distinct constellation of ASD features in 3q29 deletion syndrome: results from the 3q29 registry

Background

The 1.6 Mb 3q29 deletion is associated with neurodevelopmental and psychiatric phenotypes, including increased risk for autism spectrum disorder (ASD) and a 20 to 40-fold increased risk for schizophrenia. However, the phenotypic spectrum of the deletion, particularly with respect to ASD, remains poorly described.

Methods

We ascertained individuals with 3q29 deletion syndrome (3q29Del, “cases,” n = 93, 58.1% male) and typically developing controls (n = 64, 51.6% male) through the 3q29 registry (https://3q29deletion.patientcrossroads.org). Self-report of neuropsychiatric illness was evaluated for 93 cases. Subsets of participants were evaluated with the Social Responsiveness Scale (SRS, n = 48 cases, 56 controls), Social Communication Questionnaire (n = 33 cases, 46 controls), Autism Spectrum Screening Questionnaire (n = 24 cases, 35 controls), and Achenbach Behavior Checklists (n = 48 cases, 57 controls).

Results

3q29Del cases report a higher prevalence of autism diagnoses versus the general population (29.0% vs. 1.47%, p < 2.2E− 16). Notably, 3q29 deletion confers a greater influence on risk for ASD in females (OR = 41.8, p = 4.78E− 05) than in males (OR = 24.6, p = 6.06E− 09); this is aligned with the reduced male:female bias from 4:1 in the general population to 2:1 in our study sample. Although 71% of cases do not report a diagnosis of ASD, there is evidence of significant social disability (3q29Del SRS T-score = 71.8, control SRS T-score = 45.9, p = 2.16E− 13). Cases also report increased frequency of generalized anxiety disorder compared to controls (28.0% vs. 6.2%, p = 0.001), which is mirrored by elevated mean scores on the Achenbach diagnostic and statistical manual-oriented sub-scales (p < 0.001). Finally, cases show a distinct constellation of ASD features on the SRS as compared to idiopathic ASD, with substantially elevated Restricted Interests and Repetitive Behaviors, but only mild impairment in Social Motivation.

Conclusions

Our sample of 3q29Del is significantly enriched for ASD diagnosis, especially among females, and features of autism may be present even when an ASD diagnosis is not reported. Further, the constellation of ASD features in this population is distinct from idiopathic ASD, with substantially less impaired social motivation. Our study implies that ASD evaluation should be the standard of care for individuals with 3q29Del. From a research perspective, the distinct ASD subtype present in 3q29Del is an ideal entry point for expanding understanding of ASD.

Electronic supplementary material

The online version of this article (10.1186/s13229-019-0281-5) contains supplementary material, which is available to authorized users.

Association between Copy Number Variation and Response to Social Skills Training in Autism Spectrum Disorder

Challenges in social communication and interaction are core features of autism spectrum disorder (ASD) for which social skills group training (SSGT) is a commonly used intervention. SSGT has shown modest and heterogeneous effects. One of the major genetic risk factors in ASD is rare copy number variation (CNV). However, limited information exists whether CNV profiles could be used to aid intervention decisions. Here, we analyzed the rare genic CNV carrier status for 207 children, of which 105 received SSGT and 102 standard care as part of a randomized clinical trial for SSGT. We found that being a carrier of rare genic CNV did not have an impact on the SSGT outcome measured by the parent-report Social Responsiveness Scale (SRS). However, when stratifying by pathogenicity and size of the CNVs, we identified that carriers of clinically significant and large genic CNVs (>500 kb) showed inferior SRS outcomes at post-intervention (P = 0.047 and P = 0.036, respectively) and follow-up (P = 0.008 and P = 0.072, respectively) when adjusting for standard care effects. Our study provides preliminary evidence that carriers of clinically significant and large genic CNVs might not benefit as much from SSGT as non-carriers. Our results indicate that genetic information might help guide the modifications of interventions in ASD.

Machine learning in mental health: a scoping review of methods and applications

BACKGROUND

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

METHODS

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

RESULTS

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

CONCLUSIONS

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Genotype-phenotype associations in children with copy number variants associated with high neuropsychiatric risk in the UK (IMAGINE-ID): a case-control cohort study

BACKGROUND

Several copy number variants (CNVs) are associated with a high risk of neurodevelopmental and psychiatric disorders (referred to as ND-CNVs). We aimed to characterise the effect of ND-CNVs on childhood development and investigate whether different ND-CNVs lead to distinct and specific patterns of cognitive and behavioural outcomes.

METHODS

In this case-control study, we used data from the Intellectual Disability and Mental Health: Assessing the Genomic Impact on Neurodevelopment (IMAGINE-ID) study. Children aged 4 years and older with pathogenic CNV or single nucleotide variants were recruited via the UK National Health Service (NHS) medical genetic clinic network and via patient support groups to complete broad online phenotyping, from whom children aged 6-19 years with at least one of a specific group of ND-CNVs (1q21.1 [proximal duplication, and distal deletion and duplication], 2p16.3 deletion, 9q34.3 deletion, 15q11.2 deletion, 15q13.3 deletion and duplication, 16p11.2 [proximal deletion and duplication, and distal deletion], and 22q11.2 deletion and duplication) and their families were approached for a deep phenotyping, home-based assessment, and we report on this sample here. We invited siblings of index children to participate as controls, for whom the presence of ND-CNVs was excluded by use of microarray results and also medical records where possible. We systematically assessed the children for psychiatric disorders and broader traits of neurodevelopmental, cognitive, and psychopathological origin and compared results of ND-CNV carriers with control siblings to test the hypothesis that phenotypes would differ by genotype, both quantitatively in terms of severity and qualitatively in the pattern of associated impairments.

FINDINGS

Between Oct 1, 2014, and Dec 31, 2018, of 2819 children recruited, 258 (9%) had one ND-CNV of interest, with 13 CNVs across nine loci, and underwent a home-based assessment. 106 control siblings were enrolled. 186 (80%) of ND-CNV carriers met criteria for one or more psychiatric disorder (odds ratio [OR] 13·8, 95% CI 7·2-26·3, compared with controls). The risk of attention-deficit hyperactivity disorder (OR 6·9, 3·2-15·1), oppositional defiant disorder (OR 3·6, 1·4-9·4), any anxiety disorder (OR 2·9, 1·2-6·7), and autism spectrum disorder traits (OR 44·1, 15·3-127·5) was particularly high compared with controls. ND-CNV carriers were impaired across all neurodevelopmental, cognitive, and psychopathological traits compared with controls. Only moderate quantitative and qualitative differences in phenotypic profile were found between genotypes. Overall, the range of phenotypes was broadly similar for all ND-CNV genotypes. Traits did show some evidence of genotypic specificity, with rank-based analyses showing moderate qualitative and quantitative profile differences between ND-CNVs; however, the specific genotype accounted for a low proportion of variance in cognitive and behavioural outcomes (approximately 5-20% depending on the trait).

INTERPRETATION

The 13 ND-CNVs studied have a similar range of adverse effects on childhood neurodevelopment, despite subtle quantitative and qualitative differences. Genomic risk for neuropsychiatric disorder has pleiotropic effects on multiple processes and neural circuits and indicates that future research should avoid being narrowly focused on single phenotypes.

FUNDING

UK Medical Research Council and Medical Research Foundation.

From bedside to bench and back: Translating ASD models

Autism spectrum disorders (ASD) represent a heterogeneous group of disorders defined by deficits in social interaction/communication and restricted interests, behaviors, or activities. Models of ASD, developed based on clinical data and observations, are used in basic science, the "bench," to better understand the pathophysiology of ASD and provide therapeutic options for patients in the clinic, the "bedside." Translational medicine creates a bridge between the bench and bedside that allows for clinical and basic science discoveries to challenge one another to improve the opportunities to bring novel therapies to patients. From the clinical side, biomarker work is expanding our understanding of possible mechanisms of ASD through measures of behavior, genetics, imaging modalities, and serum markers. These biomarkers could help to subclassify patients with ASD in order to better target treatments to a more homogeneous groups of patients most likely to respond to a candidate therapy. In turn, basic science has been responding to developments in clinical evaluation by improving bench models to mechanistically and phenotypically recapitulate the ASD phenotypes observed in clinic. While genetic models are identifying novel therapeutics targets at the bench, the clinical efforts are making progress by defining better outcome measures that are most representative of meaningful patient responses. In this review, we discuss some of these challenges in translational research in ASD and strategies for the bench and bedside to bridge the gap to achieve better benefits to patients.

Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods

We investigated whether machine learning methods could potentially identify a subgroup of persons with autism spectrum disorder (ASD) who show vitamin B6 responsiveness by selected phenotype variables. We analyzed the existing data from our intervention study with 17 persons. First, we focused on signs and biomarkers that have been identified as candidates for vitamin B6 responsiveness indicators. Second, we conducted hypothesis testing among these selected variables and their combinations. Finally, we further investigated the results by conducting cluster analyses with two different algorithms, affinity propagation and k-medoids. Statistically significant variables for vitamin B6 responsiveness, including combination of hypersensitivity to sound and clumsiness, and plasma glutamine level, were included. As an a priori variable, the Pervasive Developmental Disorders Autism Society Japan Rating Scale (PARS) scores was also included. The affinity propagation analysis showed good classification of three potential vitamin B6-responsive persons with ASD. The k-medoids analysis also showed good classification. To our knowledge, this is the first study to attempt to identify subgroup of persons with ASD who show specific treatment responsiveness using selected phenotype variables. We applied machine learning methods to further investigate these variables' ability to identify this subgroup of ASD, even when only a small sample size was available.

Understanding the pediatric psychiatric phenotype of 22q11.2 deletion syndrome

The purpose of this article is to provide an overview of current insights into the neurodevelopmental and psychiatric manifestations of 22q11.2 deletion syndrome (22q11DS) in children and adolescents. The pediatric neuropsychiatric expression of 22q11DS is characterized by high variability, both interindividual and intraindividual (different expressions over the lifespan). Besides varying levels of intellectual disability, the prevalence of autism spectrum disorders, attention deficit disorders, anxiety disorders, and psychotic disorders in young individuals with 22q11DS is significantly higher than in the general population, or in individuals with idiopathic intellectual disability. Possible explanations for this observed phenotypic variability will be discussed, including genetic pleiotropy, gene-environment interactions, the age-dependency of phenotypes, but also the impact of assessment and ascertainment bias as well as the limitations of our current diagnostic classification system. The implications inferred by these observations aforementioned bear direct relevance to both scientists and clinicians. Observations regarding the neuropsychiatric manifestations in individuals with 22q11DS exemplify the need for a dimensional approach to neuropsychiatric assessment, in addition to our current categorical diagnostic classification system. The potential usefulness of 22q11DS as a genetic model to study the early phases of schizophrenia as well as the phenomenon of neuropsychiatric pleiotropy observed in many CNV's will be delineated. From a clinical perspective, the importance of regular neuropsychiatric evaluations with attention to symptoms not always captured in diagnostic categories and of maintaining equilibrium between individual difficulties and competencies and environmental demands will be discussed.

Genomics in neurodevelopmental disorders: an avenue to personalized medicine

Despite the remarkable number of scientific breakthroughs of the last 100 years, the treatment of neurodevelopmental disorders (e.g., autism spectrum disorder, intellectual disability) remains a great challenge. Recent advancements in genomics, such as whole-exome or whole-genome sequencing, have enabled scientists to identify numerous mutations underlying neurodevelopmental disorders. Given the few hundred risk genes that have been discovered, the etiological variability and the heterogeneous clinical presentation, the need for genotype—along with phenotype-based diagnosis of individual patients has become a requisite. In this review we look at recent advancements in genomic analysis and their translation into clinical practice.

The identification of genetic mutations associated with neurodevelopmental disorders (NDDs) along with routine diagnosis based on patients’ characteristics is aiding the delivery of personalized therapies. Dora Tarlungeanu and Gaia Novarino at the Institute of Science and Technology in Klosterneuburg, Austria, review recent advances in genetic technologies, such as whole exome sequencing, that can lead to early intervention, guide choice of treatment and prompt genetic counseling. Introducing the mutations associated with NDDs into model organisms or stem cells is revealing some of the mechanisms underlying NDDs and enabling the evaluation of novel therapeutic strategies that target core symptoms of the disorders. To accelerate the implementation of individualized treatments for NDD the authors highlight the need to adopt interdisciplinary research approaches and to keep clinical staff updated on the latest findings in NDD genetics.

Abnormal mTOR Activation in Autism

The mechanistic target of rapamycin (mTOR) is an important signaling hub that integrates environmental information regarding energy availability and stimulates anabolic molecular processes and cell growth. Abnormalities in this pathway have been identified in several syndromes in which autism spectrum disorder (ASD) is highly prevalent. Several studies have investigated mTOR signaling in developmental and neuronal processes that, when dysregulated, could contribute to the development of ASD. Although many potential mechanisms still remain to be fully understood, these associations are of great interest because of the clinical availability of mTOR inhibitors. Clinical trials evaluating the efficacy of mTOR inhibitors to improve neurodevelopmental outcomes have been initiated.

Genetic testing including targeted gene panel in a diverse clinical population of children with autism spectrum disorder: Findings and implications

BACKGROUND

Genetic testing of children with autism spectrum disorder (ASD) is now standard in the clinical setting, with American College of Medical Genetics and Genomics (ACMGG) guidelines recommending microarray for all children, fragile X testing for boys and additional gene sequencing, including PTEN and MECP2, in appropriate patients. Increasingly, testing utilizing high throughput sequencing, including gene panels and whole exome sequencing, are offered as well.

METHODS

We performed genetic testing including microarray, fragile X testing and targeted gene panel, consistently sequencing 161 genes associated with ASD risk, in a clinical population of 100 well characterized children with ASD. Frequency of rare variants identified in individual genes was compared with that reported in the Exome Aggregation Consortium (ExAC) database.

RESULTS

We did not diagnose any conditions with complete penetrance for ASD; however, copy number variants believed to contribute to ASD risk were identified in 12%. Eleven children were found to have likely pathogenic variants on gene panel, yet, after careful analysis, none was considered likely causative of disease. KIRREL3 variants were identified in 6.7% of children compared to 2% in ExAC, suggesting a potential role for KIRREL3 variants in ASD risk. Children with KIRREL3 variants more often had minor facial dysmorphism and intellectual disability. We also observed an increase in rare variants in TSC2. However, analysis of variant data from the Simons Simplex Collection indicated that rare variants in TSC2 occur more commonly in specific racial/ethnic groups, which are more prevalent in our population than in the ExAC database.

CONCLUSION

The yield of genetic testing including microarray, fragile X (boys) and targeted gene panel was 12%. Gene panel did not increase diagnostic yield; however, we found an increase in rare variants in KIRREL3. Our findings reinforce the need for racial/ethnic diversity in large-scale genomic databases used to identify variants that contribute to disease risk.

Identification of Developmental and Behavioral Markers Associated With Genetic Abnormalities in Autism Spectrum Disorder

OBJECTIVE

Aside from features associated with risk of neurogenetic syndromes in general (e.g., cognitive impairment), limited progress has been made in identifying phenotype-genotype relationships in autism spectrum disorder (ASD). The objective of this study was to extend work in the Simons Simplex Collection by comparing the phenotypic profiles of ASD probands with or without identified de novo loss of function mutations or copy number variants in high-confidence ASD-associated genes or loci.

METHOD

Analyses preemptively accounted for documented differences in sex and IQ in affected individuals with de novo mutations by matching probands with and without these genetic events on sex, IQ, and age before comparing them on multiple behavioral domains.

RESULTS

Children with de novo mutations (N=112) had a greater likelihood of motor delay during early development (later age at walking), but they were less impaired on certain measures of ASD core symptoms (parent-rated social communication abnormalities and clinician-rated diagnostic certainty about ASD) in later childhood. These children also showed relative strengths in verbal and language abilities, including a smaller discrepancy between nonverbal and verbal IQ and a greater likelihood of having achieved fluent language (i.e., regular use of complex sentences).

CONCLUSIONS

Children with ASD with de novo mutations may exhibit a "muted" symptom profile with respect to social communication and language deficits relative to those with ASD with no identified genetic abnormalities. Such findings suggest that examining early milestone differences and standardized testing results may be helpful in etiologic efforts, and potentially in clinical differentiation of various subtypes of ASD, but only if developmental and demographic variables are properly accounted for first.

[Genetic findings in autism spectrum disorders]

Autism spectrum disorders (ASD) are pervasive developmental disorders comprising problems in social interaction, communication, and stereotyped behavior and interests. They show a prevalence of around 0.8% in children, adolescents, and adults, and a skewed sex distribution (about 4:1 = male:female). ASD are predominantly genetically determined disorders. Heritability estimates from twin studies range between 64 and 91%. Recurrence risk in siblings is 20-fold elevated. De novo and inherited monogenetic disorders, mutations, sex chromosomal abnormalities, cytogenetic and imprinting disorders as well as common variants are associated with ASD. Genetic disorders implicating a specific additional intervention are of specific clinical relevance. Genetic testing and counselling should be provided for all families and individuals with ASD. This article gives an overview on current basic genetic research in ASD, its clinical relevance and genetic counselling in ASD.

Building a genetic risk model for bipolar disorder from genome-wide association data with random forest algorithm

A genetic risk score could be beneficial in assisting clinical diagnosis for complex diseases with high heritability. With large-scale genome-wide association (GWA) data, the current study constructed a genetic risk model with a machine learning approach for bipolar disorder (BPD). The GWA dataset of BPD from the Genetic Association Information Network was used as the training data for model construction, and the Systematic Treatment Enhancement Program (STEP) GWA data were used as the validation dataset. A random forest algorithm was applied for pre-filtered markers, and variable importance indices were assessed. 289 candidate markers were selected by random forest procedures with good discriminability; the area under the receiver operating characteristic curve was 0.944 (0.935–0.953) in the training set and 0.702 (0.681–0.723) in the STEP dataset. Using a score with the cutoff of 184, the sensitivity and specificity for BPD was 0.777 and 0.854, respectively. Pathway analyses revealed important biological pathways for identified genes. In conclusion, the present study identified informative genetic markers to differentiate BPD from healthy controls with acceptable discriminability in the validation dataset. In the future, diagnosis classification can be further improved by assessing more comprehensive clinical risk factors and jointly analysing them with genetic data in large samples.

Phenotyping, Etiological Factors, and Biomarkers: Toward Precision Medicine in Autism Spectrum Disorders

Despite the progress made in understanding the biology of autism spectrum disorder (ASD), effective biological interventions for the core symptoms remain elusive. Because of the etiological heterogeneity of ASD, identification of a "one-size-fits-all" treatment approach will likely continue to be challenging. A meeting was convened at the University of Missouri and the Thompson Center to discuss strategies for stratifying patients with ASD for the purpose of moving toward precision medicine. The "white paper" presented here articulates the challenges involved and provides suggestions for future solutions.

Support vector machine model of developmental brain gene expression data for prioritization of Autism risk gene candidates

MOTIVATION

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders with clinical heterogeneity and a substantial polygenic component. High-throughput methods for ASD risk gene identification produce numerous candidate genes that are time-consuming and expensive to validate. Prioritization methods can identify high-confidence candidates. Previous ASD gene prioritization methods have focused on a priori knowledge, which excludes genes with little functional annotation or no protein product such as long non-coding RNAs (lncRNAs).

RESULTS

We have developed a support vector machine (SVM) model, trained using brain developmental gene expression data, for the classification and prioritization of ASD risk genes. The selected feature model had a mean accuracy of 76.7%, mean specificity of 77.2% and mean sensitivity of 74.4%. Gene lists comprised of an ASD risk gene and adjacent genes were ranked using the model's decision function output. The known ASD risk genes were ranked on average in the 77.4th, 78.4th and 80.7th percentile for sets of 101, 201 and 401 genes respectively. Of 10,840 lncRNA genes, 63 were classified as ASD-associated candidates with a confidence greater than 0.95. Genes previously associated with brain development and neurodevelopmental disorders were prioritized highly within the lncRNA gene list.

CONTACT

liangjw@clemson.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Symptom profiles of autism spectrum disorder in tuberous sclerosis complex

OBJECTIVE

To determine the extent to which deficits associated with autism spectrum disorder (ASD) in toddlers with tuberous sclerosis complex (TSC) overlap with those in toddlers with nonsyndromic ASD (nsASD) and to examine cognitive function and epilepsy severity in toddlers with TSC and comorbid ASD. This is the endpoint analysis from a longitudinal investigation of ASD risk factors in children with TSC.

METHODS

Measures included the Autism Diagnostic Observation Schedule (ADOS), the Mullen Scales of Early Learning, and clinical epilepsy variables. A repeated-measures analysis of variance was performed with between-subjects factor of group (typically developing, TSC/no ASD, TSC/ASD, nsASD) and within-subjects factors of individual ADOS item scores in the social communication and repetitive behavior/restricted interest domains. Within the TSC group, comparisons of epilepsy characteristics and cognitive domains were performed using independent-samples t tests.

RESULTS

Children with TSC/ASD demonstrated a profile of social communication impairment that had complete convergence with nsASD. Measured social communication impairments included gestures, pointing, eye contact, responsive social smile, and shared enjoyment. This convergence was observed despite the high comorbidity between ASD and cognitive impairment in TSC.

CONCLUSIONS

This study supports the clinical diagnosis of ASD in young children with TSC and demonstrates remarkable convergence of autism symptoms between TSC/ASD and nsASD. Our results strongly suggest the need for early intervention in toddlers with TSC, with treatment strategies targeting social communication function as well as broader developmental domains, before the onset of autism symptoms.

Large-scale phenome analysis defines a behavioral signature for Huntington's disease genotype in mice

Rapid technological advances for the frequent monitoring of health parameters have raised the intriguing possibility that an individual's genotype could be predicted from phenotypic data alone. Here we used a machine learning approach to analyze the phenotypic effects of polymorphic mutations in a mouse model of Huntington's disease that determine disease presentation and age of onset. The resulting model correlated variation across 3,086 behavioral traits with seven different CAG-repeat lengths in the huntingtin gene (Htt). We selected behavioral signatures for age and CAG-repeat length that most robustly distinguished between mouse lines and validated the model by correctly predicting the repeat length of a blinded mouse line. Sufficient discriminatory power to accurately predict genotype required combined analysis of >200 phenotypic features. Our results suggest that autosomal dominant disease-causing mutations could be predicted through the use of subtle behavioral signatures that emerge in large-scale, combinatorial analyses. Our work provides an open data platform that we now share with the research community to aid efforts focused on understanding the pathways that link behavioral consequences to genetic variation in Huntington's disease.

Advances and Future Directions for Tuberous Sclerosis Complex Research: Recommendations From the 2015 Strategic Planning Conference

On March 10 to March 12, 2015, the National Institute of Neurological Disorders and Stroke and the Tuberous Sclerosis Alliance sponsored a workshop in Bethesda, Maryland, to assess progress and new opportunities for research in tuberous sclerosis complex with the goal of updating the 2003 Research Plan for Tuberous Sclerosis (http://www.ninds.nih.gov/about_ninds/plans/tscler_research_plan.htm). In addition to the National Institute of Neurological Disorders and Stroke and Tuberous Sclerosis Alliance, participants in the strategic planning effort and workshop included representatives from six other Institutes of the National Institutes of Health, the Department of Defense Tuberous Sclerosis Complex Research Program, and a broad cross-section of basic scientists and clinicians with expertise in tuberous sclerosis complex along with representatives from the pharmaceutical industry. Here we summarize the outcomes from the extensive premeeting deliberations and final workshop recommendations, including (1) progress in the field since publication of the initial 2003 research plan for tuberous sclerosis complex, (2) the key gaps, needs, and challenges that hinder progress in tuberous sclerosis complex research, and (3) a new set of research priorities along with specific recommendations for addressing the major challenges in each priority area. The new research plan is organized around both short-term and long-term goals with the expectation that progress toward specific objectives can be achieved within a five to ten year time frame.

The Role of mGluR Copy Number Variation in Genetic and Environmental Forms of Syndromic Autism Spectrum Disorder

While abnormal signaling mediated through metabotropic glutamate receptor 5 (mGluR5) is involved in the pathophysiology of Autism Spectrum Disorder (ASD), Fragile X Syndrome and Tuberous Sclerosis, the role of other mGluRs and their associated signaling network genes in syndromic ASD is unknown. This study sought to determine whether mGluR Copy Number Variants (CNV's) were overrepresented in children with syndromic ASD and if mGluR "second hit" confers additional risk for ASD in 22q11.2 Deletion Syndrome (22q11DS). To determine whether mGluR network CNV'S are enriched in syndromic ASD, we examined microarrays from children with ASD (n = 539). Patient categorization (syndromic vs nonsyndromic) was done via blinded medical chart review in mGluR positive and randomly selected mGluR negative cases. 11.5% of ASD had mGluR CNV's vs. 3.2% in controls (p < 0.001). Syndromic ASD was more prevalent in children with mGluR CNVs (74% vs 16%, p < 0.001). A comparison cohort with 22q11DS (n = 25 with ASD, n = 50 without ASD), all haploinsufficient for mGluR network gene RANBP1, were evaluated for "second mGluR hits". 20% with 22q11.2DS + ASD had "second hits" in mGluR network genes vs 2% in 22q11.2DS-ASD (p < 0.014). We propose that altered RANBP1 expression may provide a mechanistic link for several seemingly unrelated genetic and environmental forms of ASD.

Autism spectrum disorder profile in neurofibromatosis type I

Neurofibromatosis Type 1 (NF1) is a common autosomal dominant single-gene disorder, in which the co-occurrence of autism spectrum disorder (ASD) has attracted considerable research interest recently with prevalence estimates of 21-40%. However, detailed characterization of the ASD behavioral phenotype in NF1 is still lacking. This study characterized the phenotypic profile of ASD symptomatology presenting in 4-16 year old children with NF1 (n = 36) using evidence from parent-rated Social Responsiveness Scale and researcher autism diagnostic observation Scale-2. Compared to IQ-matched reference groups of children with autism and ASD, the NF1 profile shows overall similarity but improved eye contact, less repetitive behaviors and better language skills.

Neurobiology of rodent self-grooming and its value for translational neuroscience

Self-grooming is a complex innate behaviour with an evolutionarily conserved sequencing pattern and is one of the most frequently performed behavioural activities in rodents. In this Review, we discuss the neurobiology of rodent self-grooming, and we highlight studies of rodent models of neuropsychiatric disorders--including models of autism spectrum disorder and obsessive compulsive disorder--that have assessed self-grooming phenotypes. We suggest that rodent self-grooming may be a useful measure of repetitive behaviour in such models, and therefore of value to translational psychiatry. Assessment of rodent self-grooming may also be useful for understanding the neural circuits that are involved in complex sequential patterns of action.

Early Infant Development and Intervention for Autism Spectrum Disorder

OBJECTIVE

The objective is to overview recent findings on early detection/diagnosis of autism spectrum disorders, as well as clinical trials of early interventions for toddlers at risk for/diagnosed with autism spectrum disorder.

FINDINGS

Prospective studies of infants at high risk of autism spectrum disorder have yielded significant advances in understanding early development in autism spectrum disorder. Findings from prospective studies indicate that abnormalities in social communication and repetitive behaviors emerge during the second year, whereas additional "prodromal features" (motor and sensory abnormalities) emerge in the first year. Subsequently, exciting progress has been made in establishing the efficacy of autism spectrum disorder-specific interventions for toddlers as young as 15 months. Finally, efforts occur to characterize autism spectrum disorder-specific characteristics in genetic syndromes with concurrent autism spectrum disorder symptomatology.

CONCLUSION

Substantial progress in characterizing early developmental trajectories as well as the identification of specific behavioral markers has aided early detection. Work remains to ensure that research findings are translated into clinical practice for uptake in the health care system.

Behavioral, perceptual, and neural alterations in sensory and multisensory function in autism spectrum disorder

Although sensory processing challenges have been noted since the first clinical descriptions of autism, it has taken until the release of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in 2013 for sensory problems to be included as part of the core symptoms of autism spectrum disorder (ASD) in the diagnostic profile. Because sensory information forms the building blocks for higher-order social and cognitive functions, we argue that sensory processing is not only an additional piece of the puzzle, but rather a critical cornerstone for characterizing and understanding ASD. In this review we discuss what is currently known about sensory processing in ASD, how sensory function fits within contemporary models of ASD, and what is understood about the differences in the underlying neural processing of sensory and social communication observed between individuals with and without ASD. In addition to highlighting the sensory features associated with ASD, we also emphasize the importance of multisensory processing in building perceptual and cognitive representations, and how deficits in multisensory integration may also be a core characteristic of ASD.