scholarly journals Analysis of the effects of rare variants on splicing identifies alterations in GABAA receptor genes in autism spectrum disorder individuals

2012 ◽  
Vol 21 (7) ◽  
pp. 749-756 ◽  
Author(s):  
Amélie Piton ◽  
Loubna Jouan ◽  
Daniel Rochefort ◽  
Sylvia Dobrzeniecka ◽  
Karine Lachapelle ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Gabaa Receptor ◽  
Rare Variants ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Contribution of Multiple Inherited Variants to Autism Spectrum Disorder (ASD) in a Family with 3 Affected Siblings

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1053
Author(s):  
Jasleen Dhaliwal ◽  
Ying Qiao ◽  
Kristina Calli ◽  
Sally Martell ◽  
Simone Race ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Rare Variants ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Compound Heterozygous ◽  
Variable Effect ◽  
Gabaergic Neurotransmission ◽  
High Heritability ◽  
Compound Heterozygous Variants

Autism Spectrum Disorder (ASD) is the most common neurodevelopmental disorder in children and shows high heritability. However, how inherited variants contribute to ASD in multiplex families remains unclear. Using whole-genome sequencing (WGS) in a family with three affected children, we identified multiple inherited DNA variants in ASD-associated genes and pathways (RELN, SHANK2, DLG1, SCN10A, KMT2C and ASH1L). All are shared among the three children, except ASH1L, which is only present in the most severely affected child. The compound heterozygous variants in RELN, and the maternally inherited variant in SHANK2, are considered to be major risk factors for ASD in this family. Both genes are involved in neuron activities, including synaptic functions and the GABAergic neurotransmission system, which are highly associated with ASD pathogenesis. DLG1 is also involved in synapse functions, and KMT2C and ASH1L are involved in chromatin organization. Our data suggest that multiple inherited rare variants, each with a subthreshold and/or variable effect, may converge to certain pathways and contribute quantitatively and additively, or alternatively act via a 2nd-hit or multiple-hits to render pathogenicity of ASD in this family. Additionally, this multiple-hits model further supports the quantitative trait hypothesis of a complex genetic, multifactorial etiology for the development of ASDs.

scholarly journals Genetic disruption of the autism spectrum disorder risk gene PLAUR induces GABAA receptor subunit changes

Neuroscience ◽  
2010 ◽  
Vol 168 (3) ◽  
pp. 797-810 ◽  
Author(s):  
K.L. Eagleson ◽  
M.C. Gravielle ◽  
L.J. Schlueter McFadyen-Ketchum ◽  
S.J. Russek ◽  
D.H. Farb ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Gabaa Receptor ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Receptor Subunit ◽  
Risk Gene ◽  
Autism Spectrum Disorder Risk ◽  
Disorder Risk

scholarly journals Involvement of the 14-3-3 Gene Family in Autism Spectrum Disorder and Schizophrenia: Genetics, Transcriptomics and Functional Analyses

2020 ◽  
Vol 9 (6) ◽  
pp. 1851
Author(s):  
Bàrbara Torrico ◽  
Ester Antón-Galindo ◽  
Noèlia Fernàndez-Castillo ◽  
Eva Rojo-Francàs ◽  
Sadaf Ghorbani ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Psychiatric Disorders ◽  
Rare Variants ◽  
Neurological Diseases ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Risk Variants ◽  
Whole Exome ◽  
Target Binding ◽  
Functional Analyses

The 14-3-3 protein family are molecular chaperones involved in several biological functions and neurological diseases. We previously pinpointed YWHAZ (encoding 14-3-3ζ) as a candidate gene for autism spectrum disorder (ASD) through a whole-exome sequencing study, which identified a frameshift variant within the gene (c.659-660insT, p.L220Ffs*18). Here, we explored the contribution of the seven human 14-3-3 family members in ASD and other psychiatric disorders by investigating the: (i) functional impact of the 14-3-3ζ mutation p.L220Ffs*18 by assessing solubility, target binding and dimerization; (ii) contribution of common risk variants in 14-3-3 genes to ASD and additional psychiatric disorders; (iii) burden of rare variants in ASD and schizophrenia; and iv) 14-3-3 gene expression using ASD and schizophrenia transcriptomic data. We found that the mutant 14-3-3ζ protein had decreased solubility and lost its ability to form heterodimers and bind to its target tyrosine hydroxylase. Gene-based analyses using publicly available datasets revealed that common variants in YWHAE contribute to schizophrenia (p = 6.6 × 10−7), whereas ultra-rare variants were found enriched in ASD across the 14-3-3 genes (p = 0.017) and in schizophrenia for YWHAZ (meta-p = 0.017). Furthermore, expression of 14-3-3 genes was altered in post-mortem brains of ASD and schizophrenia patients. Our study supports a role for the 14-3-3 family in ASD and schizophrenia.

scholarly journals Comprehensive Analysis of Rare Variants of 101 Autism-Linked Genes in a Hungarian Cohort of Autism Spectrum Disorder Patients

2019 ◽  
Vol 10 ◽  
Author(s):  
Péter Balicza ◽  
Noémi Ágnes Varga ◽  
Bence Bolgár ◽  
Klára Pentelényi ◽  
Renáta Bencsik ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Rare Variants ◽  
Autism Spectrum ◽  
Comprehensive Analysis ◽  
Spectrum Disorder

scholarly journals Can machine learning aid in identifying disease genes? The case of autism spectrum disorder

2020 ◽  
Author(s):  
Margot Gunning ◽  
Paul Pavlidis
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Genetic Association ◽  
Gene Networks ◽  
Rare Variants ◽  
Association Studies ◽  
Autism Spectrum ◽  
Biological Data ◽  
Spectrum Disorder ◽  
Disease 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 thirteen 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.

scholarly journals The female protective effect against autism spectrum disorder

2021 ◽  
Author(s):  
Emilie M. Wigdor ◽  
Daniel J. Weiner ◽  
Jakob Grove ◽  
Jack M. Fu ◽  
Wesley K. Thompson ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Protective Effect ◽  
Rare Variants ◽  
De Novo ◽  
Autism Spectrum ◽  
High Impact ◽  
Spectrum Disorder ◽  
Polygenic Risk ◽  
Female Protective Effect ◽  
Nationally Representative

Autism spectrum disorder (ASD) is diagnosed 3-4 times more frequently in males than in females. Genetic studies of rare variants support a female protective effect (FPE) against ASD. However, sex differences in common, inherited genetic risk for ASD are less studied. Leveraging the nationally representative Danish iPSYCH resource, we found siblings of female ASD cases had higher rates of ASD than siblings of male ASD cases (P < 0.01). In the Simons Simplex and SPARK collections, mothers of ASD cases carried more polygenic risk for ASD than fathers of ASD cases (P = 7.0 ⨉ 10-7). Male unaffected siblings under-inherited polygenic risk (P = 0.03); female unaffected siblings did not. Further, female ASD cases without a high-impact de novo variant over-inherited nearly three-fold the polygenic risk of male cases with a high-impact de novo (P = 0.02). Our findings support a FPE against ASD that includes common, inherited genetic variation.

scholarly journals PX-RICS-deficient mice mimic autism spectrum disorder in Jacobsen syndrome through impaired GABAA receptor trafficking

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Tsutomu Nakamura ◽  
Fumiko Arima-Yoshida ◽  
Fumika Sakaue ◽  
Yukiko Nasu-Nishimura ◽  
Yasuko Takeda ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Gabaa Receptor ◽  
Autism Spectrum ◽  
Receptor Trafficking ◽  
Spectrum Disorder ◽  
Jacobsen Syndrome ◽  
Deficient Mice

scholarly journals Genetic contributions to autism spectrum disorder

2021 ◽  
pp. 1-14
Author(s):  
A. Havdahl ◽  
M. Niarchou ◽  
A. Starnawska ◽  
M. Uddin ◽  
C. van der Merwe ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Rare Variants ◽  
De Novo ◽  
Point Mutations ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Individual Risk ◽  
Future Research ◽  
Risk Genes

Abstract Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous (de novo). More than 100 risk genes have been implicated by rare, often de novo, potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse, each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

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