1. {{Autism spectrum disorder association with mood and anxiety disorders}}. {J Paediatr Child Health};2020 (Jul);56(7):1163.
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2. Cha JY, Min SK, Yoon TH, Jee YS. {{Gross motor function and health fitness in adults with autistic spectrum disorder and intellectual disability: single-blind retrospective trial}}. {J Exerc Rehabil};2020 (Jun);16(3):258-264.
This study aimed at providing an exercise program for each type of disability after analyzing the exercise program performed by adults with intellectual disability (ID) or autistic spectrum disorder (ASD). Twenty-nine male adults voluntarily took part in this study, whose age ranged from 19 to 28 years and with an average body mass index of 23.98± 4.02 kg/m(2). The sample was divided into two groups as follows: ASD group (ASDG; n=15) and ID group (IDG, n=14). The selected tests used to measure gross motor function (GMF, locomotion and object control skills) and health fitness (body composition, flexibility, strength, muscle endurance, and cardiopulmonary endurance) were also used in previous studies. The GMF and health fitness between ASDG and IDG showed no significant differences. This study indicates that exercise programs could provide similar effects, even with other disorder types having similar symptoms.
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3. Cox SK, Jimenez BA. {{Mathematical interventions for students with autism spectrum disorder: Recommendations for practitioners}}. {Res Dev Disabil};2020 (Jul 25);105:103744.
Students with extensive support needs (ESN; i.e., autism spectrum disorder, intellectual disability, or both), have the ability to learn a variety of mathematical skills when taught using scientifically validated strategies (e.g., Bouck, Satsangi, Taber-Doughty, & Courtney, 2014; Creech-Galloway, Collins, Knight, & Bausch, 2013; Root, Browder, Saunders, & Lo, 2017). The urgency of teaching grade-aligned, mathematical standards to this population has significantly increased in the past two decades. Yet, in order to teach grade-aligned mathematics to individuals with disabilities, teachers need access to scientifically validated strategies that can be effective with this heterogeneous population. This article extends work by Fleury and colleagues (2014) by expanding their findings of interventions to teach academic content to students with ASD. We hope practitioners can use this article as a starting point when selecting scientifically validated interventions to teach mathematics to students with ESN. We highlight mathematics interventions from a variety of recent literature reviews of mathematics interventions for students with ASD, ID, or both to provide guidance for practitioners of what we know works for which students under what conditions. This article also seeks to bridge research to practice by offering recommendations for math educators serving students with disabilities in heterogeneous classrooms.
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4. De Korte MW, van den Berk-Smeekens I, van Dongen-Boomsma M, Oosterling IJ, Den Boer JC, Barakova EI, Lourens T, Buitelaar JK, Glennon JC, Staal WG. {{Self-initiations in young children with autism during Pivotal Response Treatment with and without robot assistance}}. {Autism};2020 (Jul 30):1362361320935006.
The initiation of social interaction is often defined as a core deficit of autism spectrum disorder. Optimizing these self-initiations is therefore a key component of Pivotal Response Treatment, an established intervention for children with autism spectrum disorder. However, little is known about the development of self-initiations during intervention and whether this development can be facilitated by robot assistance within Pivotal Response Treatment. The aim of this study was to (1) investigate the effect of Pivotal Response Treatment and robot-assisted Pivotal Response Treatment on self-initiations (functional and social) of young children with autism spectrum disorder over the course of intervention and (2) explore the relation between development in self-initiations and additional gains in general social-communicative skills. Forty-four children with autism spectrum disorder (aged 3-8 years) were included in this study. Self-initiations were assessed during parent-child interaction videos of therapy sessions and coded by raters who did not know which treatment (Pivotal Response Treatment or robot-assisted Pivotal Response Treatment) the child received. General social-communicative skills were assessed before start of the treatment, after 10 and 20 weeks of intervention and 3 months after the treatment was finalized. Results showed that self-initiations increased in both treatment groups, with the largest improvements in functional self-initiations in the group that received robot-assisted Pivotal Response Treatment. Increased self-initiations were related to higher parent-rated social awareness 3 months after finalizing the treatment.
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5. Frost KM, Brian J, Gengoux GW, Hardan A, Rieth SR, Stahmer A, Ingersoll B. {{Identifying and measuring the common elements of naturalistic developmental behavioral interventions for autism spectrum disorder: Development of the NDBI-Fi}}. {Autism};2020 (Jul 30):1362361320944011.
Naturalistic developmental behavioral interventions for young children with autism spectrum disorder share key elements. However, the extent of similarity between programs within this class of evidence-based interventions is unknown. There is also currently no tool that can be used to measure the implementation of their common elements. This article presents a multi-stage process which began with defining all intervention elements of naturalistic developmental behavioral interventions. Next, intervention experts identified the common elements of naturalistic developmental behavioral interventions using a survey. An observational rating scheme of those common elements, the eight-item NDBI-Fi, was developed. We evaluated the quality of the NDBI-Fi using videos from completed trials of caregiver-implemented naturalistic developmental behavioral interventions. Results showed that the NDBI-Fi measure has promise; it was sensitive to change, related to other similar measures, and demonstrated adequate agreement between raters. This unique measure has the potential to advance intervention science in autism spectrum disorder by providing a tool to measure the implementation of common elements across naturalistic developmental behavioral intervention models. Given that naturalistic developmental behavioral interventions have numerous shared strategies, this may ease clinicians’ uncertainty about choosing the « right » intervention package. It also suggests that there may not be a need for extensive training in more than one naturalistic developmental behavioral intervention. Future research should determine whether these common elements are part of other treatment approaches to better understand the quality of services children and families receive as part of usual care.
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6. González-Peñas J, Costas JC, García-Alcón A, Penzol MJ, Rodríguez J, Rodríguez-Fontenla C, Alonso-González A, Fernández-Prieto M, Carracedo Á, Arango C, Parellada M. {{Psychiatric comorbidities in Asperger syndrome are related with polygenic overlap and differ from other Autism subtypes}}. {Transl Psychiatry};2020 (Jul 30);10(1):258.
There is great phenotypic heterogeneity within autism spectrum disorders (ASD), which has led to question their classification into a single diagnostic category. The study of the common genetic variation in ASD has suggested a greater contribution of other psychiatric conditions in Asperger syndrome (AS) than in the rest of the DSM-IV ASD subtypes (Non_AS). Here, using available genetic data from previously performed genome-wide association studies (GWAS), we aimed to study the genetic overlap between five of the most related disorders (schizophrenia (SCZ), major depression disorder (MDD), attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorders (OCD) and anxiety (ANX)), and AS, comparing it with the overlap in Non_AS subtypes. A Spanish cohort of autism trios (N = 371) was exome sequenced as part of the Autism Sequencing Consortium (ASC) and 241 trios were extensively characterized to be diagnosed with AS following DSM-IV and Gillberg’s criteria (N = 39) or not (N = 202). Following exome imputation, polygenic risk scores (PRS) were calculated for ASD, SCZ, ADHD, MDD, ANX, and OCD (from available summary data from Psychiatric Genomic Consortium (PGC) repository) in the Spanish trios’ cohort. By using polygenic transmission disequilibrium test (pTDT), we reported that risk for SCZ (P(scz )= 0.008, corrected-P(SCZ) = 0.0409), ADHD (P(ADHD) = 0.021, corrected-P(ADHD) = 0.0301), and MDD (P(MDD) = 0.039, corrected-P(MDD) = 0.0501) is over-transmitted to children with AS but not to Non_AS. Indeed, agnostic clustering procedure with deviation values from pTDT tests suggested two differentiated clusters of subjects, one of which is significantly enriched in AS (P = 0.025). Subsequent analysis with S-Predixcan, a recently developed software to predict gene expression from genotype data, revealed a clear pattern of correlation between cortical gene expression in ADHD and AS (P < 0.001) and a similar strong correlation pattern between MDD and AS, but also extendable to another non-brain tissue such as lung (P < 0.001). Altogether, these results support the idea of AS being qualitatively distinct from Non_AS autism and consistently evidence the genetic overlap between AS and ADHD, MDD, or SCZ. Lien vers le texte intégral (Open Access ou abonnement)
7. Gudbrandsen M, Mann C, Bletsch A, Daly E, Murphy CM, Stoencheva V, Blackmore CE, Rogdaki M, Kushan L, Bearden CE, Murphy DGM, Craig MC, Ecker C. {{Corrigendum to: Patterns of Cortical Folding Associated with Autistic Symptoms in Carriers and Noncarriers of the 22q11.2 Microdeletion}}. {Cereb Cortex};2020 (Jul 30);30(9):5191.
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8. He C, Chen H, Uddin LQ, Erramuzpe A, Bonifazi P, Guo X, Xiao J, Chen H, Huang X, Li L, Sheng W, Liao W, Cortes JM, Duan X. {{Structure-Function Connectomics Reveals Aberrant Developmental Trajectory Occurring at Preadolescence in the Autistic Brain}}. {Cereb Cortex};2020 (Jul 30);30(9):5028-5037.
Accumulating neuroimaging evidence shows that age estimation obtained from brain connectomics reflects the level of brain maturation along with neural development. It is well known that autism spectrum disorder (ASD) alters neurodevelopmental trajectories of brain connectomics, but the precise relationship between chronological age (ChA) and brain connectome age (BCA) during development in ASD has not been addressed. This study uses neuroimaging data collected from 50 individuals with ASD and 47 age- and gender-matched typically developing controls (TDCs; age range: 5-18 years). Both functional and structural connectomics were assessed using resting-state functional magnetic resonance imaging and diffusion tensor imaging data from the Autism Brain Imaging Data Exchange repository. For each participant, BCA was estimated from structure-function connectomics through linear support vector regression. We found that BCA matched well with ChA in TDC children and adolescents, but not in ASD. In particular, our findings revealed that individuals with ASD exhibited accelerated brain maturation in youth, followed by a delay of brain development starting at preadolescence. Our results highlight the critical role of BCA in understanding aberrant developmental trajectories in ASD and provide the new insights into the pathophysiological mechanisms of this disorder.
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9. Kalvin CB, Gladstone TR, Jordan R, Rowley S, Marsh CL, Ibrahim K, Sukhodolsky DG. {{Assessing Irritability in Children with Autism Spectrum Disorder Using the Affective Reactivity Index}}. {J Autism Dev Disord};2020 (Jul 30)
Irritability is an impairing problem in children with ASD that may be associated with other behavioral and emotional concerns. The Affective Reactivity Index (ARI) is a parent-rated measure of irritability widely used in children with mood disorders, however, its utility in children with ASD remains unclear. In this study, we examined ARI parent ratings in children with ASD and contributions of parent-rated anxiety and noncompliance to irritability measured by the ARI. Participants included 81 children with ASD, aged 8-16 years. Results suggest that both anxiety and noncompliance contribute to irritability, but that anxiety only contributes to irritability in the absence of noncompliance. Further, the ARI is likely to be a useful measure of irritability in children with ASD.
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10. Kennedy T, Rinker D, Broadie K. {{Genetic background mutations drive neural circuit hyperconnectivity in a fragile X syndrome model}}. {BMC Biol};2020 (Jul 30);18(1):94.
BACKGROUND: Neural circuits are initially assembled during development when neurons synapse with potential partners and later refined as appropriate connections stabilize into mature synapses while inappropriate contacts are eliminated. Disruptions to this synaptogenic process impair connectivity optimization and can cause neurodevelopmental disorders. Intellectual disability (ID) and autism spectrum disorder (ASD) are often characterized by synaptic overgrowth, with the maintenance of immature or inappropriate synapses. Such synaptogenic defects can occur through mutation of a single gene, such as fragile X mental retardation protein (FMRP) loss causing the neurodevelopmental disorder fragile X syndrome (FXS). FXS represents the leading heritable cause of ID and ASD, but many other genes that play roles in ID and ASD have yet to be identified. RESULTS: In a Drosophila FXS disease model, one dfmr1(50M) null mutant stock exhibits previously unreported axonal overgrowths at developmental and mature stages in the giant fiber (GF) escape circuit. These excess axon projections contain both chemical and electrical synapse markers, indicating mixed synaptic connections. Extensive analyses show these supernumerary synapses connect known GF circuit neurons, rather than new, inappropriate partners, indicating hyperconnectivity within the circuit. Despite the striking similarities to well-characterized FXS synaptic defects, this new GF circuit hyperconnectivity phenotype is driven by genetic background mutations in this dfmr1(50M) stock. Similar GF circuit synaptic overgrowth is not observed in independent dfmr1 null alleles. Bulked segregant analysis (BSA) was combined with whole genome sequencing (WGS) to identify the quantitative trait loci (QTL) linked to neural circuit hyperconnectivity. The results reveal 8 QTL associated with inappropriate synapse formation and maintenance in the dfmr1(50M) mutant background. CONCLUSIONS: Synaptogenesis is a complex, precisely orchestrated neurodevelopmental process with a large cohort of gene products coordinating the connectivity, synaptic strength, and excitatory/inhibitory balance between neuronal partners. This work identifies a number of genetic regions that contain mutations disrupting proper synaptogenesis within a particularly well-mapped neural circuit. These QTL regions contain potential new genes involved in synapse formation and refinement. Given the similarity of the synaptic overgrowth phenotype to known ID and ASD inherited conditions, identifying these genes should increase our understanding of these devastating neurodevelopmental disease states.
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11. Kozhemiako N, Nunes AS, Vakorin V, Iarocci G, Ribary U, Doesburg SM. {{Alterations in Local Connectivity and Their Developmental Trajectories in Autism Spectrum Disorder: Does Being Female Matter?}}. {Cereb Cortex};2020 (Jul 30);30(9):5166-5179.
Autism spectrum disorder (ASD) is diagnosed more often in males with a ratio of 1:4 females/males. This bias is even stronger in neuroimaging studies. There is a growing evidence suggesting that local connectivity and its developmental trajectory is altered in ASD. Here, we aim to investigate how local connectivity and its age-related trajectories vary with ASD in both males and females. We used resting-state fMRI data from the ABIDE I and II repository: males (n = 102) and females (n = 92) with ASD, and typically developing males (n = 104) and females (n = 92) aged between 6 and 26. Local connectivity was quantified as regional homogeneity. We found increases in local connectivity in participants with ASD in the somatomotor and limbic networks and decreased local connectivity within the default mode network. These alterations were more pronounced in females with ASD. In addition, the association between local connectivity and ASD symptoms was more robust in females. Females with ASD had the most distinct developmental trajectories of local connectivity compared with other groups. Overall, our findings of more pronounced local connectivity alterations in females with ASD could indicate a greater etiological load for an ASD diagnosis in this group congruent with the female protective effect hypothesis.
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12. Lawrence KE, Hernandez LM, Bowman HC, Padgaonkar NT, Fuster E, Jack A, Aylward E, Gaab N, Van Horn JD, Bernier RA, Geschwind DH, McPartland JC, Nelson CA, Webb SJ, Pelphrey KA, Green SA, Bookheimer SY, Dapretto M. {{Sex Differences in Functional Connectivity of the Salience, Default Mode, and Central Executive Networks in Youth with ASD}}. {Cereb Cortex};2020 (Jul 30);30(9):5107-5120.
Autism spectrum disorder (ASD) is associated with the altered functional connectivity of 3 neurocognitive networks that are hypothesized to be central to the symptomatology of ASD: the salience network (SN), default mode network (DMN), and central executive network (CEN). Due to the considerably higher prevalence of ASD in males, however, previous studies examining these networks in ASD have used primarily male samples. It is thus unknown how these networks may be differentially impacted among females with ASD compared to males with ASD, and how such differences may compare to those observed in neurotypical individuals. Here, we investigated the functional connectivity of the SN, DMN, and CEN in a large, well-matched sample of girls and boys with and without ASD (169 youth, ages 8-17). Girls with ASD displayed greater functional connectivity between the DMN and CEN than boys with ASD, whereas typically developing girls and boys differed in SN functional connectivity only. Together, these results demonstrate that youth with ASD exhibit altered sex differences in these networks relative to what is observed in typical development, and highlight the importance of considering sex-related biological factors and participant sex when characterizing the neural mechanisms underlying ASD.
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13. Lefort-Besnard J, Vogeley K, Schilbach L, Varoquaux G, Thirion B, Dumas G, Bzdok D. {{Patterns of autism symptoms: hidden structure in the ADOS and ADI-R instruments}}. {Transl Psychiatry};2020 (Jul 30);10(1):257.
We simultaneously revisited the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) with a comprehensive data-analytics strategy. Here, the combination of pattern-analysis algorithms and extensive data resources (n = 266 patients aged 7-49 years) allowed identifying coherent clinical constellations in and across ADI-R and ADOS assessments widespread in clinical practice. Our clustering approach revealed low- and high-severity patient groups, as well as a group scoring high only in the ADI-R domains, providing quantitative contours for the widely assumed autism subtypes. Sparse regression approaches uncovered the most clinically predictive questionnaire domains. The social and communication domains of the ADI-R showed convincing performance to predict the patients’ symptom severity. Finally, we explored the relative importance of each of the ADI-R and ADOS domains conditioning on age, sex, and fluid IQ in our sample. The collective results suggest that (i) identifying autism subtypes and severity for a given individual may be most manifested in the ADI-R social and communication domains, (ii) the ADI-R might be a more appropriate tool to accurately capture symptom severity, and (iii) the ADOS domains were more relevant than the ADI-R domains to capture sex differences.
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14. Liu R, Dong H, Wang Y, Lu X, Li Y, Xun G, Ou J, Shen Y, Xia K, Zhao J. {{Sleep Problems of Children with Autism May Independently Affect Parental Quality of Life}}. {Child Psychiatry Hum Dev};2020 (Jul 28)
The current study explored how and to what extent sleep problems in children with autism spectrum disorder (ASD) impacted their parents’ quality of life (QOL). A total of 440 ASD children and 344 age-matched typically developing (TD) children were included in the case-control designed study. In the TD group, a linear regression model showed that the Children’s Sleep Habits Questionnaire (CSHQ) total scores were negatively associated with maternal mental health summary (MCS) scores in the SF-36v2 (β = – 2.831), while in the ASD group, the CSHQ total scores were negatively associated with the parental physical health summary (PCS) scores (β = – 3.030 for mothers, β = – 3.651 for fathers). Path analysis showed that sleep problems in ASD children had both direct and indirect effects on maternal PCS scores. The results indicated that sleep problems in children with ASD might affect parental QOL differently from TD children, and act as independent impact factors on parental physical health.
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15. Pandya SP. {{Examining the Effectiveness of WhatsApp-Based Spiritual Posts on Mitigating Stress and Building Resilience, Maternal Confidence and Self-efficacy Among Mothers of Children with ASD}}. {J Autism Dev Disord};2020 (Jul 30)
Spiritual posts delivered via WhatsApp were found effective for mothers of children with ASD in mitigating parenting stress and building parental self-efficacy, confidence and resilience as compared to a control group. Intervention compliance was a prerequisite, and, middle-class mothers, highly qualified, salaried/self-employed, who perceived full support from their spouses/families and who also participated in other support groups or skill development programs benefitted most. Latent class analyses revealed six subgroups of participants likely to gain most from the intervention: middle class mothers, highly qualified, salaried/self-employed, who participated in other skill development programs, above threshold posts readers and homework doers. Spirituality built mothers’ psychological resources, but dyadic interventions may be needed for dealing with challenges and uncertainties of autistic child’s emotional/behavioral characteristics.
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16. Pearson A, Hodgetts S. {{Can cerebral lateralisation explain heterogeneity in language and increased non-right handedness in autism? A literature review}}. {Res Dev Disabil};2020 (Jul 25);105:103738.
BACKGROUND: Autism is characterised by phenotypic variability, particularly in the domains of language and handedness. However, the source of this heterogeneity is currently unclear. AIMS: To synthesise findings regarding the relationship between language, handedness, and cerebral lateralisation in autistic people and consider how future research should be conducted in order to progress our understanding of phenotypic variability. METHODS AND PROCEDURES: Following a literature search and selection process, 19 papers were included in this literature review. Studies using behavioural, structural, and functional measures of lateralisation are reviewed. OUTCOMES AND RESULTS: The studies reviewed provided consistent evidence of differential cerebral lateralisation in autistic people, and this appears to be related to between-group differences in language. Evidence relating this to handedness was less consistent. Many of the studies did not include heterogeneous samples, and/or did not specify the language process they investigated. CONCLUSIONS AND IMPLICATIONS: This review suggests that further research is needed to fully understand the relationship between cerebral lateralisation and phenotypic variability within autism. It is crucial that future studies in this area include heterogeneous samples, specify the language process they are investigating, and consider taking developmental trajectories into account.
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17. Tanner A, Dounavi K. {{The Emergence of Autism Symptoms Prior to 18 Months of Age: A Systematic Literature Review}}. {J Autism Dev Disord};2020 (Jul 30)
Pre-diagnostic intervention for autism spectrum disorder (ASD) allows symptoms to be addressed as they emerge, often between six to 18 months, rather than after the full onset of the disorder. A systematic literature review, spanning the previous six years was conducted in order to provide an updated review looking at the earliest behavior symptoms of ASD. All included studies used a prospective experimental design, reported on symptoms that emerged before 18-months of age, exclusively in children who would later receive a diagnosis, and were assessed for quality. This review is the first to address this research question through the use of a systematic research design and extends the literature by following up on recommendations for future research from previous findings.
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18. Williams CM, Peyre H, Toro R, Beggiato A, Ramus F. {{Adjusting for allometric scaling in ABIDE I challenges subcortical volume differences in autism spectrum disorder}}. {Hum Brain Mapp};2020 (Jul 30)
Inconsistencies across studies investigating subcortical correlates of autism spectrum disorder (ASD) may stem from small sample size, sample heterogeneity, and omitting or linearly adjusting for total brain volume (TBV). To properly adjust for TBV, brain allometry-the nonlinear scaling relationship between regional volumes and TBV-was considered when examining subcortical volumetric differences between typically developing (TD) and ASD individuals. Autism Brain Imaging Data Exchange I (ABIDE I; N = 654) data was analyzed with two methodological approaches: univariate linear mixed effects models and multivariate multiple group confirmatory factor analyses. Analyses were conducted on the entire sample and in subsamples based on age, sex, and full scale intelligence quotient (FSIQ). A similar ABIDE I study was replicated and the impact of different TBV adjustments on neuroanatomical group differences was investigated. No robust subcortical allometric or volumetric group differences were observed in the entire sample across methods. Exploratory analyses suggested that allometric scaling and volume group differences may exist in certain subgroups defined by age, sex, and/or FSIQ. The type of TBV adjustment influenced some reported volumetric and scaling group differences. This study supports the absence of robust volumetric differences between ASD and TD individuals in the investigated volumes when adjusting for brain allometry, expands the literature by finding no group difference in allometric scaling, and further suggests that differing TBV adjustments contribute to the variability of reported neuroanatomical differences in ASD.
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19. Yin J, Chun CA, Zavadenko NN, Pechatnikova NL, Naumova OY, Doddapaneni HV, Hu J, Muzny DM, Schaaf CP, Grigorenko EL. {{Next Generation Sequencing of 134 Children with Autism Spectrum Disorder and Regression}}. {Genes (Basel)};2020 (Jul 25);11(8)
Approximately 30% of individuals with autism spectrum disorder (ASD) experience developmental regression, the etiology of which remains largely unknown. We performed a complete literature search and identified 47 genes that had been implicated in such cases. We sequenced these genes in a preselected cohort of 134 individuals with regressive autism. In total, 16 variants in 12 genes with evidence supportive of pathogenicity were identified. They were classified as variants of uncertain significance based on ACMG standards and guidelines. Among these were recurring variants in GRIN2A and PLXNB2, variants in genes that were linked to syndromic forms of ASD (GRIN2A, MECP2, CDKL5, SCN1A,PCDH19, UBE3A, and SLC9A6), and variants in the form of oligogenic heterozygosity (EHMT1, SLC9A6, and MFSD8).
