Variants In Autism Spectrum Disorder Research Articles

Resolving complex duplication variants in autism spectrum disorder using long-read genome sequencing

Rare or de novo structural variation, primarily in the form of copy number variants, is detected in 5%–10% of autism spectrum disorder (ASD) families. While complex structural variants involving duplications can generally be detected using microarray or short-read genome sequencing (GS), these methods frequently fail to characterize breakpoints at nucleotide resolution, requiring additional molecular methods for validation and fine-mapping. Here, we use Oxford Nanopore Technologies PromethION long-read GS to characterize complex genomic rearrangements (CGRs) involving large duplications that segregate with ASD in five families. In total, we investigated 13 CGR carriers and were able to resolve all breakpoint junctions at nucleotide resolution. While all breakpoints were identified, the precise genomic architecture of one rearrangement remained unresolved with three different potential structures. The findings in two families include potential fusion genes formed through duplication rearrangements, involvingIL1RAPL1–DMDandSUPT16H–CHD8. In two of the families originating from the same geographical region, an identical rearrangement involvingANK2was identified, which likely represents a founder variant. In addition, we analyze methylation status directly from the long-read data, allowing us to assess the activity of rearranged genes and regulatory regions. Investigation of methylation across the CGRs reveals aberrant methylation status in carriers across a rearrangement affecting theCREBBPlocus. In aggregate, our results demonstrate the utility of nanopore sequencing to pinpoint CGRs associated with ASD in five unrelated families, and highlight the importance of a gene-centric description of disease-associated complex chromosomal rearrangements.

Factors Associated With Underutilization of Genetic Testing in Autism Spectrum Disorders

BackgroundWe sought to identify patient and provider factors associated with low completion of genetic testing, specifically chromosomal microarray (CMA), for autism spectrum disorder (ASD). MethodsMedical record review was conducted of children newly diagnosed with ASD without prior genetic testing at a single academic medical center from February 2015 through January 2016. ResultsOnly 41.9% of individuals with ASD completed CMA testing over at least 18 months from diagnosis (n = 140 of 334). Time to CMA completion varied, with a median of 86.5 days (interquartile range 2 to 214.5 days). Provider recommendation of genetic testing at the diagnostic visit and greater number of follow-up visits were associated with CMA completion. On multivariate regression, CMA completion was inversely associated with age (odds ratio [OR] = 0.8 for each year older, 95% confidence interval [CI] 0.7, 0.9; P = 0.001) and directly associated with intellectual disability or global developmental delay (OR = 2.2, 95% CI 1.3, 3.8; P = 0.004), first-degree relative with ASD (OR = 2.5, 95% CI 1.0, 6.0; P = 0.044), and public insurance (OR = 1.7, 95% CI 1.0, 2.9; P = 0.037). Parental concern and cost/insurance coverage were the most frequently documented barriers. ConclusionsWorkflows to support early genetic testing recommendation and ordering soon after diagnosis may increase utilization, incorporating both family and provider perspectives. Genetic counseling highlighting the utility of genetic testing across the life span, phenotypic variability of genetic disorders, and possibility of de novo variants in ASD may also improve utilization.

Three generation families: Analysis of de novo variants in autism.

De novo variants (DNVs) analysis has proven to be a powerful approach to gene discovery in Autism Spectrum Disorder (ASD), which has not yet been shown in a Brazilian ASD cohort. The relevance of inherited rare variants has also been suggested, particularly in oligogenic models. We hypothesized that three-generation analyses of DNVs could provide new insights into the relevance of de novo and inherited variants across generations. To accomplish this goal, we performed whole-exome sequencing of 33 septet families composed of probands, parents, and grandparents (n = 231 individuals) and compared DNV rates (DNVr) between generations and those from two control cohorts. The DNVr in the probands (DNVr = 1.16) was marginally higher than in parents (DNVr = 0.60; p = 0.054), and in controls (DNVr = 0.68; p = 0.035, congenital heart disorder and DNVr = 0.70; p = 0.047, unaffected ASD siblings from Simons Simplex Collection). Moreover, most of the DNVs were found to have paternal origin in both generations (84.6%). Finally, we observed that 40% (6/15) of the DNVs in parents transmitted for probands are in ASD or ASD candidate genes, representing recently emerged risk variants to ASD in their families and suggest ZNF536, MSL2 and HDAC9 as ASD candidate genes. We did not observe an enrichment of risk variants nor sex bias of transmitted variants in the three generations, that can be due to sample size. These results further reinforce the relevance of de novo variants in ASD.

Molecular and in silico analyses of SYN III gene variants in autism spectrum disorder.

Defects in neurotransmission and synaptogenesis are noteworthy in the pathogenesis of ASD. Synapsin III (SYN III) is defined as a synaptic vesicle protein that plays an important role in synaptogenesis and regulation of neurotransmitter release and neurite outgrowth. Therefore, SYN III may associate with many neurodevelopmental diseases, including ASD. The aim of this study was to investigate whether the SYN III gene -631 C > G (rs133946) and -196 G > A (rs133945) polymorphisms are associated with susceptibility to ASD. SYN III variants and the risk of ASD were investigated in 26 healthy children and 24 ASD children. SYN III gene variants were genotyped by PCR-RFLP methods. The differences in genotype and allele frequencies between the ASD and control groups were calculated using the chi-square (χ2). We analysed the SYN III gene using web-based tools. Our results suggest that the presence of the AA genotype of the SYN III -196 G > A (rs133945) polymorphism affects the characteristics and development of ASD in children (p = 0.012). SYN III -631 C > G (rs133946) polymorphism was not associated with ASD (p = 0.524). We have shown the prediction of gene-gene interaction that SYN III is co-expressed with 17 genes, physical interaction with 3 genes, and co-localization with 12 genes. The importance of different genes (SYN I, II, III, GABRD, NOS1AP, GNAO1) for ASD pathogenesis was revealed by GO analysis. Considering the role of SYN III and related genes, especially in the synaptic vesicle pathway and neurotransmission, its effect on ASD can be further investigated.

DGH-GO: dissecting the genetic heterogeneity of complex diseases using gene ontology

BackgroundComplex diseases such as neurodevelopmental disorders (NDDs) exhibit multiple etiologies. The multi-etiological nature of complex-diseases emerges from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine approaches to complex genetic disorders.ResultsHere, we present an interactive and user-friendly application, called DGH-GO. DGH-GO allows biologists to dissect the genetic heterogeneity of complex diseases by stratifying the putative disease-causing genes into clusters that may contribute to distinct disease outcome development. It can also be used to study the shared etiology of complex-diseases. DGH-GO creates a semantic similarity matrix for the input genes by using Gene Ontology (GO). The resultant matrix can be visualized in 2D plots using different dimension reduction methods (T-SNE, Principal component analysis, umap and Principal coordinate analysis). In the next step, clusters of functionally similar genes are identified from genes functional similarities assessed through GO. This is achieved by employing four different clustering methods (K-means, Hierarchical, Fuzzy and PAM). The user may change the clustering parameters and explore their effect on stratification immediately. DGH-GO was applied to genes disrupted by rare genetic variants in Autism Spectrum Disorder (ASD) patients. The analysis confirmed the multi-etiological nature of ASD by identifying four clusters of genes that were enriched for distinct biological mechanisms and clinical outcome. In the second case study, the analysis of genes shared by different NDDs showed that genes causing multiple disorders tend to aggregate in similar clusters, indicating a possible shared etiology.ConclusionDGH-GO is a user-friendly application that allows biologists to study the multi-etiological nature of complex diseases by dissecting their genetic heterogeneity. In summary, functional similarities, dimension reduction and clustering methods, coupled with interactive visualization and control over analysis allows biologists to explore and analyze their datasets without requiring expert knowledge on these methods. The source code of proposed application is available at https://github.com/Muh-Asif/DGH-GO

Stratification of Children with Autism Spectrum Disorder Through Fusion of Temporal Information in Eye-gaze Scan-Paths

Background: Looking pattern differences are shown to separate individuals with Autism Spectrum Disorder (ASD) and Typically Developing (TD) controls. Recent studies have shown that, in children with ASD, these patterns change with intellectual and social impairments, suggesting that patterns of social attention provide indices of clinically meaningful variation in ASD. Method: We conducted a naturalistic study of children with ASD (n = 55) and typical development (TD, n = 32). A battery of eye-tracking video stimuli was used in the study, including Activity Monitoring (AM), Social Referencing (SR), Theory of Mind (ToM), and Dyadic Bid (DB) tasks. This work reports on the feasibility of spatial and spatiotemporal scanpaths generated from eye-gaze patterns of these paradigms in stratifying ASD and TD groups. Algorithm: This article presents an approach for automatically identifying clinically meaningful information contained within the raw eye-tracking data of children with ASD and TD. The proposed mechanism utilizes combinations of eye-gaze scan-paths (spatial information), fused with temporal information and pupil velocity data and Convolutional Neural Network (CNN) for stratification of diagnosis (ASD or TD). Results: Spatial eye-gaze representations in the form of scanpaths in stratifying ASD and TD (ASD vs. TD: DNN: 74.4%) are feasible. These spatial eye-gaze features, e.g., scan-paths, are shown to be sensitive to factors mediating heterogeneity in ASD: age (ASD: 2–4 y/old vs. 10–17 y/old CNN: 80.5%), gender (Male vs. Female ASD: DNN: 78.0%) and the mixture of age and gender (5–9 y/old Male vs. 5–9 y/old Female ASD: DNN:98.8%). Limiting scan-path representations temporally increased variance in stratification performance, attesting to the importance of the temporal dimension of eye-gaze data. Spatio-Temporal scan-paths that incorporate velocity of eye movement in their images of eye-gaze are shown to outperform other feature representation methods achieving classification accuracy of 80.25%. Conclusion: The results indicate the feasibility of scan-path images to stratify ASD and TD diagnosis in children of varying ages and gender. Infusion of temporal information and velocity data improves the classification performance of our deep learning models. Such novel velocity fused spatio-temporal scan-path features are shown to be able to capture eye gaze patterns that reflect age, gender, and the mixed effect of age and gender, factors that are associated with heterogeneity in ASD and difficulty in identifying robust biomarkers for ASD.

Characterization of De Novo Promoter Variants in Autism Spectrum Disorder with Massively Parallel Reporter Assays.

Autism spectrum disorder (ASD) is a common, complex, and highly heritable condition with contributions from both common and rare genetic variations. While disruptive, rare variants in protein-coding regions clearly contribute to symptoms, the role of rare non-coding remains unclear. Variants in these regions, including promoters, can alter downstream RNA and protein quantity; however, the functional impacts of specific variants observed in ASD cohorts remain largely uncharacterized. Here, we analyzed 3600 de novo mutations in promoter regions previously identified by whole-genome sequencing of autistic probands and neurotypical siblings to test the hypothesis that mutations in cases have a greater functional impact than those in controls. We leveraged massively parallel reporter assays (MPRAs) to detect transcriptional consequences of these variants in neural progenitor cells and identified 165 functionally high confidence de novo variants (HcDNVs). While these HcDNVs are enriched for markers of active transcription, disruption to transcription factor binding sites, and open chromatin, we did not identify differences in functional impact based on ASD diagnostic status.

Investigating the Role of Noncoding De Novo Single-Nucleotide Variants in Autism Spectrum Disorder

Investigating the Role of Noncoding De Novo Single-Nucleotide Variants in Autism Spectrum Disorder

Integrated exome and transcriptome analysis prioritizes MAP4K4 de novo frameshift variants in autism spectrum disorder as a novel disease–gene association

The application of next-generation sequencing (NGS) to clinical practice is still hampered by the ability to interpret the clinical relevance of novel variants and the difficulty of evaluating their effect in specific tissues. Here, we applied integrated genomic approaches for interrogating blood samples of two unrelated individuals with neurodevelopmental disorders and identified a novel neuro-pathogenic role for the Mitogen-Activated Protein Kinase 4 gene (MAP4K4). In particular, we identified two novel frameshift variants in coding exons expressed in the blood and neuronal isoforms. Both variants were predicted to generate non-sense-mediated decay. By transcriptome analysis, we simultaneously demonstrated the deleterious effect of the identified variants on the splicing activity and stability of MAP4K4 mRNA. Therefore, we propose MAP4K4 as a novel causative gene for non-syndromic and syndromic neurodevelopmental disorders. Altogether, we prove the efficacy of an integrated approach of exome and transcriptome sequencing in the resolution of undiagnosed cases by leveraging the analysis of variants in genes expressed in peripheral blood.

Detection of Clinically Relevant Genetic Variants in Children with Autism Spectrum Disorder by Whole Exome Sequencing

Autism spectrum disorder (ASD) is a neurodevelopment disorder which is related with a good number of genetic mutations. There is a lack of data regarding the genetic framework of children with ASD in Bangladesh. Hence, this study was conducted to detect clinically relevant genetic variants of ASD. A total of13 children with ASD aged between 2-10 years were included in this study, DSM-5 was used to confirm the diagnosis of ASD and to exclude other neurodevelopment, emotional, and behavioral disorders. Whole Exome Sequencing was done under the supervision of a geneticist. Mean age of participants was 5.54±3.13 years with male predominance (61.5℅). Positive family history of neurodevelopment disorder was present in 46.2%. The average loss of acquired skill of study participants was 26.45±14.50 months. Most of the patients had delayed development of vocalization (92.3%), body gesture (61.5%), and spontaneous phrases (69.2%). Attention deficit hyperactivity disorder (ADHD) was the most common (46.2%) co-morbidity Majority patients (76.92%, n = 10) had presence of gene mutation, wherein 38.46% (n = 5) had variants of uncertain clinical significance (VOUS), 30.77% (n = 4) had likely pathogenic, and only 1 patient (7.69%) had pathogenic gene mutation. In this study, a good number of pathogenic genes related to ASD namely TENM4, ASXL1, CHD3, TSC2 and CACNA1H were detected. Larger multicenter study is recommended. J. of Sci. and Tech. Res. 3(1): 49-60, 2021

LINE-1 and Alu methylation signatures in autism spectrum disorder and their associations with the expression of autism-related genes

Long interspersed nucleotide element-1 (LINE-1) and Alu elements are retrotransposons whose abilities cause abnormal gene expression and genomic instability. Several studies have focused on DNA methylation profiling of gene regions, but the locus-specific methylation of LINE-1 and Alu elements has not been identified in autism spectrum disorder (ASD). Here we interrogated locus- and family-specific methylation profiles of LINE-1 and Alu elements in ASD whole blood using publicly-available Illumina Infinium 450 K methylation datasets from heterogeneous ASD and ASD variants (Chromodomain Helicase DNA-binding 8 (CHD8) and 16p11.2del). Total DNA methylation of repetitive elements were notably hypomethylated exclusively in ASD with CHD8 variants. Methylation alteration in a family-specific manner including L1P, L1H, HAL, AluJ, and AluS families were observed in the heterogeneous ASD and ASD with CHD8 variants. Moreover, LINE-1 and Alu methylation within target genes is inversely related to the expression level in each ASD variant. The DNA methylation signatures of the LINE-1 and Alu elements in ASD whole blood, as well as their associations with the expression of ASD-related genes, have been identified. If confirmed in future larger studies, these findings may contribute to the identification of epigenomic biomarkers of ASD.

Impulsivity Moderates the Effect of Neurofeedback Training on the Contingent Negative Variation in Autism Spectrum Disorder.

BackgroundThe contingent negative variation (CNV) is a well-studied indicator of attention- and expectancy-related processes in the human brain. An abnormal CNV amplitude has been found in diverse neurodevelopmental psychiatric disorders. However, its role as a potential biomarker of successful clinical interventions in autism spectrum disorder (ASD) remains unclear.MethodsIn this randomized controlled trial, we investigated how the CNV changes following an intensive neurofeedback training. Therefore, twenty-one adolescents with ASD underwent 24 sessions of slow cortical potential (SCP) neurofeedback training. Twenty additional adolescents with ASD formed a control group and received treatment as usual. CNV waveforms were obtained from a continuous performance test (CPT), which all adolescents performed before and after the corresponding 3-month long training period. In order to utilize all available neural time series, trial-based area under the curve values for all four electroencephalogram (EEG) channels were analyzed with a hierarchical Bayesian model. In addition, the model included impulsivity, inattention, and hyperactivity as potential moderators of change in CNV.ResultsOur model implies that impulsivity moderates the effects of neurofeedback training on CNV depending on group. In the control group, the average CNV amplitude decreased or did not change after treatment as usual. In the experimental group, the CNV changed depending on the severity of comorbid impulsivity symptoms. The average CNV amplitude of participants with low impulsivity scores decreased markedly, whereas the average CNV amplitude of participants with high impulsivity increased.ConclusionThe degree of impulsivity seems to play a crucial role in the changeability of the CNV following an intensive neurofeedback training. Therefore, comorbid symptomatology should be recorded and analyzed in future EEG-based brain training interventions.Clinical Trial Registrationhttps://www.drks.de, identifier DRKS00012339.

Drosophila functional screening of de novo variants in autism uncovers damaging variants and facilitates discovery of rare neurodevelopmental diseases.

SUMMARYIndividuals with autism spectrum disorder (ASD) exhibit an increased burden of de novo mutations (DNMs) in a broadening range of genes. While these studies have implicated hundreds of genes in ASD pathogenesis, which DNMs cause functional consequences in vivo remains unclear. We functionally test the effects of ASD missense DNMs using Drosophila through “humanization” rescue and overexpression-based strategies. We examine 79 ASD variants in 74 genes identified in the Simons Simplex Collection and find 38% of them to cause functional alterations. Moreover, we identify GLRA2 as the cause of a spectrum of neurodevelopmental phenotypes beyond ASD in 13 previously undiagnosed subjects. Functional characterization of variants in ASD candidate genes points to conserved neurobiological mechanisms and facilitates gene discovery for rare neurodevelopmental diseases.

Eating behavior in children with autism spectrum disorder

To study atypical eating behavior (AEB) in various variants of autism spectrum disorder (ASD) to determine the prognosis and therapeutic tactics. One hundred and eighty patients (110 girls, 70 boys), aged 2 to 5 years (mean age 4 years), with a diagnosis of «Childhood autism» (F84.02), «Atypical autism» (F84.1), «Asperger Syndrome» (F84.5) and the presence of AEB were examined. Three clinical variants of ASD accompanied by AEB were identified: catatonic-regressive (n=75, 41%), catatonic (n=81, 45%) and hyperdynamic (n=24, 13%). The most pronounced AEB were in the catatonic-regressive variant, and the least in the hyperdynamic variant of ASD. The severity of AEB is associated with the severity of cognitive dysontogenesis. The characteristics of AEB are associated with the leading clinical syndrome of ASD. The severity of catatonia and regression affects the depth of AEB, which leads to secondary somatic disorders. Specialists of various profiles should participate in medical work for successful rehabilitation and prevention of somatic complications.

Diagnostic Yield and Economic Implications of Whole-Exome Sequencing for ASD Diagnosis in Israel.

Whole-exome sequencing (WES) is an effective approach to identify the susceptibility of genetic variants of autism spectrum disorder (ASD). The Israel Ministry of Health supports WES as an adjunct tool for ASD diagnosis, despite its unclear diagnostic yield and cost effectiveness. To address this knowledge gap, we applied WES to a population-based sample of 182 Bedouin and Jewish children with ASD from southern Israel, and assessed its yield in a gene panel of 205 genes robustly associated with ASD. We then compared the incremental cost-effectiveness ratios (ICERs) for an ASD diagnosis by WES, chromosomal microarray analysis (CMA), and CMA + WES. Overall, 32 ASD candidate variants were detected in 28 children, corresponding to an overall WES diagnostic yield of 15.4%. Interestingly, the diagnostic yield was significantly higher for the Bedouin children than for the Jewish children, i.e., 27.6% vs. 11.1% (p = 0.036). The most cost-effective means for genetic testing was the CMA alone, followed closely by the CMA + WES strategy (ICER = USD 117 and USD 124.8 per child). Yet, WES alone could become more cost effective than the other two approaches if there was to be a 25% increase in its yield or a 50% decrease in its cost. These findings suggest that WES should be recommended to facilitate ASD diagnosis in Israel, especially for highly consanguineous populations, such as the Bedouin.

Single-cell transcriptome identifies molecular subtype of autism spectrum disorder impacted by de novo loss-of-function variants regulating glial cells

BackgroundIn recent years, several hundred autism spectrum disorder (ASD) implicated genes have been discovered impacting a wide range of molecular pathways. However, the molecular underpinning of ASD, particularly from the point of view of ‘brain to behaviour’ pathogenic mechanisms, remains largely unknown.MethodsWe undertook a study to investigate patterns of spatiotemporal and cell type expression of ASD-implicated genes by integrating large-scale brain single-cell transcriptomes (> million cells) and de novo loss-of-function (LOF) ASD variants (impacting 852 genes from 40,122 cases).ResultsWe identified multiple single-cell clusters from three distinct developmental human brain regions (anterior cingulate cortex, middle temporal gyrus and primary visual cortex) that evidenced high evolutionary constraint through enrichment for brain critical exons and high pLI genes. These clusters also showed significant enrichment with ASD loss-of-function variant genes (p < 5.23 × 10–11) that are transcriptionally highly active in prenatal brain regions (visual cortex and dorsolateral prefrontal cortex). Mapping ASD de novo LOF variant genes into large-scale human and mouse brain single-cell transcriptome analysis demonstrate enrichment of such genes into neuronal subtypes and are also enriched for subtype of non-neuronal glial cell types (astrocyte, p < 6.40 × 10–11, oligodendrocyte, p < 1.31 × 10–09).ConclusionAmong the ASD genes enriched with pathogenic de novo LOF variants (i.e. KANK1, PLXNB1), a subgroup has restricted transcriptional regulation in non-neuronal cell types that are evolutionarily conserved. This association strongly suggests the involvement of subtype of non-neuronal glial cells in the pathogenesis of ASD and the need to explore other biological pathways for this disorder.

Prevalence of autism spectrum disorder in a large, diverse metropolitan area: Variation by sociodemographic factors.

Autism spectrum disorder (ASD) prevalence estimates have varied by region. In this study, ASD prevalence, based on active case finding from multiple sources, was determined at the county and school district levels in the New Jersey metropolitan area. Among children born in 2008, residing in a four-county area and enrolled in public school in 2016, ASD prevalence was estimated to be 36 per 1000, but was significantly higher in one region-54 per 1000 and greater than 70 per 1000, in multiple school districts. Significant variation in ASD prevalence by race/ethnicity, socioeconomic status (SES), and school district size was identified. Highest prevalence was in mid-SES communities, contrary to expectation. Prevalence among Hispanic children was lower than expected, indicating a disparity in identification. Comprehensive surveillance should provide estimates at the county and town levels to appreciate ASD trends, identify disparities in detection or treatment, and explore factors influencing change in prevalence. LAY SUMMARY: We found autism prevalence to be 3.6% in New Jersey overall, but higher in one region (5.4%) and in multiple areas approaching 7.0%. We identified significant variation in autism spectrum disorder (ASD) prevalence by race/ethnicity, socioeconomic status (SES) and school district size. Mapping prevalence in smaller, well-specified, regions may be useful to better understand the true scope of ASD, disparities in ASD detection and the factors impacting ASD prevalence estimation.

The Role of Ion Channel-Related Genes in Autism Spectrum Disorder: A Study Using Next-Generation Sequencing.

The clinical heterogeneity of autism spectrum disorder (ASD) is closely associated with the diversity of genes related to ASD pathogenesis. With their low effect size, it has been hard to define the role of common variants of genes in ASD phenotype. In this study, we reviewed genetic results and clinical scores widely used for ASD diagnosis to investigate the role of genes in ASD phenotype considering their functions in molecular pathways. Genetic data from next-generation sequencing (NGS) were collected from 94 participants with ASD. We analyzed enrichment of cellular processes and gene ontology using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). We compared clinical characteristics according to genetic functional characteristics. We found 266 genes containing nonsense, frame shift, missense, and splice site mutations. Results from DAVID revealed significant enrichment for “ion channel” with an enrichment score of 8.84. Moreover, ASD participants carrying mutations in ion channel-related genes showed higher total IQ (p = 0.013) and lower repetitive, restricted behavior (RRB)-related scores (p = 0.003) and mannerism subscale of social responsiveness scale scores, compared to other participants. Individuals with variants in ion channel genes showed lower RRB scores, suggesting that ion channel genes might be relatively less associated with RRB pathogenesis. These results contribute to understanding of the role of common variants in ASD and could be important in the development of precision medicine of ASD.

Prevalence and phenotypic impact of rare potentially damaging variants in autism spectrum disorder

BackgroundThe Autism Sequencing Consortium identified 102 high-confidence autism spectrum disorder (ASD) genes, showing that individuals with ASD and with potentially damaging single nucleotide variation (pdSNV) in these genes had lower cognitive levels and delayed age at walking, when compared to ASD participants without pdSNV. Here, we made use of a Swedish sample of individuals with ASD (called PAGES, for Population-Based Autism Genetics & Environment Study) to evaluate the frequency of pdSNV and their impact on medical and psychiatric phenotypes, using an epidemiological frame and universal health reporting. We then combine findings with those for potentially damaging copy number variation (pdCNV).MethodsSNV and CNV calls were generated from whole-exome sequencing and chromosome microarray data, respectively. Birth and medical register data were used to collect phenotypes.ResultsOf 808 individuals assessed by sequencing, 69 (9%) had pdSNV in the 102 ASC genes, and 144 (18%) had pdSNV in the 102 ASC genes or in a larger set of curated neurodevelopmental genes (from the Deciphering Developmental Disorders study, the gene2phenotype database, and the Radboud University gene lists). Three or more individuals had pdSNV in GRIN2B, POGZ, SATB1, DYNC1H1, SCN8A, or CREBBP. In comparison, out of the 996 individuals from whom CNV were called, 105 (11%) carried one or more pdCNV, including four or more individuals with CNV in the recurrent 15q11q13, 22q11.2, and 16p11.2 loci. Carriers of pdSNV were more likely to have intellectual disability (ID) and epilepsy, while carriers of pdCNV showed increased rates of congenital anomalies and scholastic skill disorders. Carriers of either pdSNV or pdCNV were more likely to have ID, scholastic skill disorders, and epilepsy.LimitationsThe cohort only included individuals with autistic disorder, the more severe form of ASD, and phenotypes are defined from medical registers. Not all genes studied are definitively ASD genes, and we did not have de novo information to aid in classification.ConclusionsIn this epidemiological sample, rare pdSNV were more common than pdCNV and the combined yield of potentially damaging variation was substantial at 27%. The results provide compelling rationale for the use of high-throughout sequencing as part of routine clinical workup for ASD and support the development of precision medicine in ASD.

Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning.

BackgroundIn the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children.ObjectiveRecent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening.MethodsAfter preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants.ResultsThe selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic individuals from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic individuals from nonautistic individuals. Our classifier demonstrated a considerable improvement of ~13% in terms of classification accuracy compared to standard autism screening tools.ConclusionsCommon variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.