scholarly journals SHANK3 Genotype Mediates Speech and Language Phenotypes in a Nonclinical Population

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Christina Manning ◽  
Peter L. Hurd ◽  
Silven Read ◽  
Bernard Crespi
Keyword(s):  
Neurodevelopmental Disorders ◽  
Rare Variants ◽  
Large Population ◽  
Autism Spectrum ◽  
Psychological Effects ◽  
Speech And Language ◽  
Schizotypal Personality ◽  
Model Studies ◽  
Language Studies ◽  
Schizotypal Personality Questionnaire

Mutations affecting the synaptic-scaffold gene SHANK3 represent the most common genetic causes of autism with intellectual disability, accounting for about 1-2% of cases. Rare variants of this gene have also been associated with schizophrenia, and its deletion results in the autistic condition known as Phelan–McDermid syndrome. Despite the importance of SHANK3 as a paradigmatic gene mediating neurodevelopmental disorders, its psychological effects in nonclinical populations have yet to be studied. We genotyped the nonsynonymous, functional SHANK3 SNP rs9616915 in a large population of typical individuals scored for autism spectrum traits (the Autism Quotient, AQ) and schizotypy spectrum traits (the Schizotypal Personality Questionnaire, SPQ-BR). Males, but not females, showed significant genotypic effects for the SPQ-BR subscale associated with speech and language: Odd Speech. These findings, in conjunction with animal model studies showing vocalization and auditory effects of SHANK3 mutations, and studies indicating severe language alterations and speech-associated white matter tract abnormalities in Phelan–McDermid syndrome, suggest that SHANK3 differentially affects the development and expression of human language and speech. Imaging genetic and speech-language studies of typical individuals carrying different genotypes of rs9616915 should provide novel insights into the neurological and psychological bases of speech and language alterations among individuals with SHANK3 mutations and Phelan–McDermid syndrome.

scholarly journals A genetic locus for paranoia

2018 ◽  
Vol 14 (1) ◽  
pp. 20170694 ◽  
Author(s):  
Bernard Crespi ◽  
Silven Read ◽  
Iiro Salminen ◽  
Peter Hurd
Keyword(s):  
Genetic Model ◽  
Genetic Locus ◽  
Large Population ◽  
Autism Spectrum ◽  
Psychological Effects ◽  
Imprinted Genes ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Altered Expression ◽  
Schizophrenia Spectrum

The psychological effects of brain-expressed imprinted genes in humans are virtually unknown. Prader–Willi syndrome (PWS) is a neurogenetic condition mediated by genomic imprinting, which involves high rates of psychosis characterized by hallucinations and paranoia, as well as autism. Altered expression of two brain-expressed imprinted genes, MAGEL2 and NDN , mediates a suite of PWS-related phenotypes, including behaviour, in mice. We phenotyped a large population of typical individuals for schizophrenia-spectrum and autism-spectrum traits, and genotyped them for the single-nucleotide polymorphism rs850807, which is putatively functional and linked with MAGEL2 and NDN . Genetic variation in rs850807 was strongly and exclusively associated with the ideas of reference subscale of the schizophrenia spectrum, which is best typified as paranoia. These findings provide a single-locus genetic model for analysing the neurological and psychological bases of paranoid thinking, and implicate imprinted genes, and genomic conflicts, in human mentalistic thought.

scholarly journals Fragile X syndrome, the search for a targeted treatment

2019 ◽  
Vol 5 (1) ◽  
pp. 12
Author(s):  
Shimriet Zeidler ◽  
Rob Willemsen
Keyword(s):  
Fragile X Syndrome ◽  
Neurodevelopmental Disorders ◽  
Fragile X ◽  
Short Review ◽  
Autism Spectrum ◽  
Targeted Treatment ◽  
Adequate Treatment ◽  
Model Studies ◽  
Physical Problems ◽  
Spectrum Disorders

Fragile X syndrome (FXS), the most common monogenetic cause of intellectual disability and autism spectrum disorders, is characterized by behavioral and physical problems. There is currently no adequate treatment available. While animal model studies seemed extremely promising, no success has been achieved in the larger clinical trials with human FXS patients. This short review describes the steps that have been taken in the development of a targeted treatment for FXS. Possible reasons for the lack of translation between animal models and human FXS patients are being explored and solutions are being proposed. The FXS story illustrates pitfalls and possibilities in translational research, that might especially be applicable for other neurodevelopmental disorders as well. 

scholarly journals Insufficient Evidence for “Autism-Specific” Genes

2020 ◽  
Author(s):  
Scott M. Myers ◽  
Thomas D. Challman ◽  
Raphael Bernier ◽  
Thomas Bourgeron ◽  
Wendy K. Chung ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Rare Variant ◽  
Effect Size ◽  
Neurodevelopmental Disorders ◽  
Rare Variants ◽  
Autism Spectrum ◽  
Insufficient Evidence ◽  
Large Effect Size ◽  
Published Evidence ◽  
Contradictory Data

Despite the evidence that deleterious variants in the same genes are implicated across multiple neurodevelopmental disorders, there has been considerable interest in identifying genes that, when mutated, confer risk that is largely specific for autism spectrum disorder (ASD). Here, we review recent efforts to identify “autism-specific” genes, which focus on rare variants of large effect size that are thought to account for the observed phenotype in participants, present a divergent interpretation of the published evidence, and provide additional contradictory data. There is currently insufficient evidence to establish ASD-specificity of any genes based on large-effect rare variant data.

Unravelling the phenotypic tangle of autistic, schizotypal and social anxiety traits: A Network Analysis

2020 ◽  
Author(s):  
Orestis Zavlis ◽  
Myles Jones
Keyword(s):  
Network Analysis ◽  
Social Anxiety ◽  
Factor Structure ◽  
Adult Population ◽  
Autism Spectrum ◽  
Underlying Structure ◽  
Schizotypal Personality ◽  
The Social ◽  
Trait Level ◽  
General Adult Population

Substantial overlap exists between schizophrenia and autism spectrum disorders, with part of that overlap hypothesised to be due to comorbid social anxiety. The current paper investigates the interactions and factor structure of these disorders at a personality trait level, through the lens of a network model. The items of the Autism Quotient (AQ), Schizotypal Personality Questionnaire Brief-Revised (SPQ-BR), and the Liebowitz Social Anxiety Scale (L-SAS) were combined and completed by 345 members of the general adult population. An Exploratory Graph Analysis (EGA) on the AQ-SPQ-BR combined inventory revealed two communities (factors), which reflected the general autism and schizotypal phenotypes. An additional EGA on all inventories validated the AQ-SPQ-BR factor structure and revealed another community, Social Anxiety (L-SAS). A Network Analysis (NA) on all inventories revealed several moderately central subscales, which collectively reflected the social-interpersonal impairments of the three disorders. The current results suggest that a combination of recent network- and traditional factor-analytic techniques may present a fruitful approach to understanding the underlying structure as well as relation of different psychopathologies.

Developmental milestones in early childhood and genetic liability to neurodevelopmental disorders

2020 ◽  
Author(s):  
Laurie John Hannigan ◽  
Ragna Bugge Askeland ◽  
Helga Ask ◽  
Martin Tesli ◽  
Elizabeth Corfield ◽  
...  
Keyword(s):  
Language Development ◽  
Motor Development ◽  
Neurodevelopmental Disorders ◽  
Autism Spectrum ◽  
Probit Regression ◽  
Developmental Milestones ◽  
Genetic Liability ◽  
Polygenic Scores ◽  
Walking Age ◽  
Children First

BackgroundEarly developmental milestones, such as the age at first walking or talking, are associated with later diagnoses of neurodevelopmental disorders, but the relationship to genetic risk for neurodevelopmental disorders are unknown. Here, we investigate associations between genetic liability to autism spectrum disorder (autism), attention deficit hyperactivity disorder (ADHD), and schizophrenia and attainment of early-life language and motor development milestones.MethodsWe use data from a genotyped sub-set (N = 15 205) of children in the Norwegian Mother, Father and Child Cohort Study (MoBa). In this sample, we calculate polygenic scores for autism; ADHD and schizophrenia and predict maternal reports of children’s age at first walking and talking, motor delays at 18 months, language delays at 3 years, and a generalized measure of concerns about development. We use linear and probit regression models in a multi-group framework to test for sex differences.ResultsADHD polygenic scores predicted earlier walking age in both males and females (β=-0.037, pFDR=0.001), and earlier first use of sentences (β=-0.087, pFDR=0.032) but delayed language development at 3 years in females only (β=0.194, pFDR=0.001). Additionally, we found evidence that autism polygenic scores were associated with later walking (β=0.027, pFDR=0.024) and motor delays at 18 months (β = 0.065, pFDR=0.028). Schizophrenia polygenic scores were associated with a measure of general concerns about development at 3 years in females only (β=0.132, pFDR=0.024).ConclusionsGenetic liabilities for neurodevelopmental disorders show some specific associations with measures of early motor and language development in the general population, including the age at which children first walk and talk. Associations are generally small and occasionally in unexpected directions. Sex differences are evident in some instances, but clear patterns across different polygenic scores and outcomes are hard to discern. These findings suggest that genetic susceptibility for neurodevelopmental disorders is manifested in the timing of developmental milestones in infancy.

scholarly journals LSD1 enzyme inhibitor TAK-418 unlocks aberrant epigenetic machinery and improves autism symptoms in neurodevelopmental disorder models

2021 ◽  
Vol 7 (11) ◽  
pp. eaba1187
Author(s):  
Rina Baba ◽  
Satoru Matsuda ◽  
Yuuichi Arakawa ◽  
Ryuji Yamada ◽  
Noriko Suzuki ◽  
...  
Keyword(s):  
Gene Expression ◽  
Enzyme Activity ◽  
Neurodevelopmental Disorders ◽  
Neurodevelopmental Disorder ◽  
Specific Inhibitor ◽  
Autism Spectrum ◽  
Poly I:C ◽  
Control Of Gene Expression ◽  
Epigenetic Machinery ◽  
The Brain

Persistent epigenetic dysregulation may underlie the pathophysiology of neurodevelopmental disorders, such as autism spectrum disorder (ASD). Here, we show that the inhibition of lysine-specific demethylase 1 (LSD1) enzyme activity normalizes aberrant epigenetic control of gene expression in neurodevelopmental disorders. Maternal exposure to valproate or poly I:C caused sustained dysregulation of gene expression in the brain and ASD-like social and cognitive deficits after birth in rodents. Unexpectedly, a specific inhibitor of LSD1 enzyme activity, 5-((1R,2R)-2-((cyclopropylmethyl)amino)cyclopropyl)-N-(tetrahydro-2H-pyran-4-yl)thiophene-3-carboxamide hydrochloride (TAK-418), almost completely normalized the dysregulated gene expression in the brain and ameliorated some ASD-like behaviors in these models. The genes modulated by TAK-418 were almost completely different across the models and their ages. These results suggest that LSD1 enzyme activity may stabilize the aberrant epigenetic machinery in neurodevelopmental disorders, and the inhibition of LSD1 enzyme activity may be the master key to recover gene expression homeostasis. TAK-418 may benefit patients with neurodevelopmental disorders.

scholarly journals Using Sibling Designs to Understand Neurodevelopmental Disorders: From Genes and Environments to Prevention Programming

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Mark Wade ◽  
Heather Prime ◽  
Sheri Madigan
Keyword(s):  
Neurodevelopmental Disorders ◽  
Broad Class ◽  
Psychosocial Health ◽  
Recurrence Risk ◽  
Autism Spectrum ◽  
Functional Neuroanatomy ◽  
Early Screening ◽  
Environmental Liabilities ◽  
Hyperactivity Disorder ◽  
Significant Bearing

Neurodevelopmental disorders represent a broad class of childhood neurological conditions that have a significant bearing on the wellbeing of children, families, and communities. In this review, we draw on evidence from two common and widely studied neurodevelopmental disorders—autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD)—to demonstrate the utility of genetically informed sibling designs in uncovering the nature and pathogenesis of these conditions. Specifically, we examine how twin, recurrence risk, and infant prospective tracking studies have contributed to our understanding of genetic and environmental liabilities towards neurodevelopmental morbidity through their impact on neurocognitive processes and structural/functional neuroanatomy. It is suggested that the siblings of children with ASD and ADHD are at risk not only of clinically elevated problems in these areas, but also of subthreshold symptoms and/or subtle impairments in various neurocognitive skills and other domains of psychosocial health. Finally, we close with a discussion on the practical relevance of sibling designs and how these might be used in the service of early screening, prevention, and intervention efforts that aim to alleviate the negative downstream consequences associated with disorders of neurodevelopment.

scholarly journals “Guilt by association” is not competitive with genetic association for identifying autism risk genes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Margot Gunning ◽  
Paul Pavlidis
Keyword(s):  
Machine Learning ◽  
Genetic Association ◽  
Gene Networks ◽  
Rare Variants ◽  
Association Studies ◽  
Genetic Disorders ◽  
Autism Spectrum ◽  
Biological Data ◽  
Disease Genes ◽  
Risk Genes

AbstractDiscovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.

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