scholarly journals Rare coding variation illuminates the allelic architecture, risk genes, cellular expression patterns, and phenotypic context of autism

2021 ◽  
Author(s):  
Jack M. Fu ◽  
F. Kyle Satterstrom ◽  
Minshi Peng ◽  
Harrison Brand ◽  
Ryan L. Collins ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Developmental Disorders ◽  
De Novo ◽  
Expression Patterns ◽  
Allelic Diversity ◽  
Autism Spectrum ◽  
Functional Variation ◽  
Risk Genes ◽  
Missense Variants ◽  
Cellular Expression

Individuals with autism spectrum disorder (ASD) or related neurodevelopmental disorders (NDDs) often carry disruptive mutations in genes that are depleted of functional variation in the broader population. We build upon this observation and exome sequencing from 154,842 individuals to explore the allelic diversity of rare protein-coding variation contributing risk for ASD and related NDDs. Using an integrative statistical model, we jointly analyzed rare protein-truncating variants (PTVs), damaging missense variants, and copy number variants (CNVs) derived from exome sequencing of 63,237 individuals from ASD cohorts. We discovered 71 genes associated with ASD at a false discovery rate (FDR) ≤ 0.001, a threshold approximately equivalent to exome-wide significance, and 183 genes at FDR ≤ 0.05. Associations were predominantly driven by de novo PTVs, damaging missense variants, and CNVs: 57.4%, 21.2%, and 8.32% of evidence, respectively. Though fewer in number, CNVs conferred greater relative risk than PTVs, and repeat-mediated de novo CNVs exhibited strong maternal bias in parent-of-origin (e.g., 92.3% of 16p11.2 CNVs), whereas all other CNVs showed a paternal bias. To explore how genes associated with ASD and NDD overlap or differ, we analyzed our ASD cohort alongside a developmental delay (DD) cohort from the deciphering developmental disorders study (DDD; n=91,605 samples). We first reanalyzed the DDD dataset using the same models as the ASD cohorts, then performed joint analyses of both cohorts and identified 373 genes contributing to NDD risk at FDR ≤ 0.001 and 662 NDD risk genes at FDR ≤ 0.05. Of these NDD risk genes, 54 genes (125 genes at FDR ≤ 0.05) were unique to the joint analyses and not significant in either cohort alone. Our results confirm overlap of most ASD and DD risk genes, although many differ significantly in frequency of mutation. Analyses of single-cell transcriptome datasets showed that genes associated predominantly with DD were strongly enriched for earlier neurodevelopmental cell types, whereas genes displaying stronger evidence for association in ASD cohorts were more enriched for maturing neurons. The ASD risk genes were also enriched for genes associated with schizophrenia from a separate rare coding variant analysis of 121,570 individuals, emphasizing that these neuropsychiatric disorders share common pathways to risk.

scholarly journals MED12-Related (Neuro)Developmental Disorders: A Question of Causality

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 663
Author(s):  
Stijn van de Plassche ◽  
Arjan PM de Brouwer
Keyword(s):  
Developmental Disorders ◽  
De Novo ◽  
Expression Patterns ◽  
Mediator Complex ◽  
Gene Expression Patterns ◽  
Facial Dysmorphism ◽  
Regulation Of Transcription ◽  
Feeding Difficulties ◽  
Missense Variants ◽  
Pathogenic Variants

MED12 is a member of the Mediator complex that is involved in the regulation of transcription. Missense variants in MED12 cause FG syndrome, Lujan-Fryns syndrome, and Ohdo syndrome, as well as non-syndromic intellectual disability (ID) in hemizygous males. Recently, female patients with de novo missense variants and de novo protein truncating variants in MED12 were described, resulting in a clinical spectrum centered around ID and Hardikar syndrome without ID. The missense variants are found throughout MED12, whether they are inherited in hemizygous males or de novo in females. They can result in syndromic or nonsyndromic ID. The de novo nonsense variants resulting in Hardikar syndrome that is characterized by facial clefting, pigmentary retinopathy, biliary anomalies, and intestinal malrotation, are found more N-terminally, whereas the more C-terminally positioned variants are de novo protein truncating variants that cause a severe, syndromic phenotype consisting of ID, facial dysmorphism, short stature, skeletal abnormalities, feeding difficulties, and variable other abnormalities. This broad range of distinct phenotypes calls for a method to distinguish between pathogenic and non-pathogenic variants in MED12. We propose an isogenic iNeuron model to establish the unique gene expression patterns that are associated with the specific MED12 variants. The discovery of these patterns would help in future diagnostics and determine the causality of the MED12 variants.

scholarly journals Schizophrenia, autism spectrum disorders and developmental disorders share specific disruptive coding mutations

2020 ◽  
Author(s):  
Elliott Rees ◽  
Hugo Creeth ◽  
Hai-Gwo Hwu ◽  
Wei Chen ◽  
Ming Tsuang ◽  
...  
Keyword(s):  
Autism Spectrum Disorders ◽  
Intellectual Disability ◽  
Neurodevelopmental Disorders ◽  
Developmental Disorders ◽  
De Novo ◽  
Autism Spectrum ◽  
Risk Genes ◽  
Spectrum Disorders ◽  
Coding Variants ◽  
Shared Risk

Abstract Genes enriched for rare disruptive coding variants in schizophrenia overlap those in which disruptive mutations are associated with neurodevelopmental disorders (NDDs), particularly autism spectrum disorders and intellectual disability. However, it is unclear whether this implicates the same specific variants, or even variants with the same functional effects on shared risk genes. Here, we show that de novo mutations in schizophrenia are generally of the same functional category as those that confer risk for NDDs, and that the specific de novo mutations in NDDs are enriched in schizophrenia. These findings indicate that, in part, NDDs and schizophrenia have shared molecular aetiology, and therefore likely overlapping pathophysiology. We also observe pleiotropic effects for variants known to be pathogenic for several syndromic developmental disorders, suggesting that schizophrenia should be included among the phenotypes associated with these mutations. Collectively, our findings support the hypothesis that at least some forms of schizophrenia lie within a continuum of neurodevelopmental disorders.

scholarly journals Schizophrenia, autism spectrum disorders and developmental disorders share specific disruptive coding mutations

2020 ◽  
Author(s):  
Elliott Rees ◽  
Hugo D. J. Creeth ◽  
Hai-Gwo Hwu ◽  
Wei J. Chen ◽  
Ming Tsuang ◽  
...  
Keyword(s):  
Autism Spectrum Disorders ◽  
Intellectual Disability ◽  
Neurodevelopmental Disorders ◽  
Developmental Disorders ◽  
De Novo ◽  
Autism Spectrum ◽  
Risk Genes ◽  
Spectrum Disorders ◽  
Coding Variants ◽  
Shared Risk

AbstractGenes enriched for rare disruptive coding variants in schizophrenia overlap those in which disruptive mutations are associated with neurodevelopmental disorders (NDDs), particularly autism spectrum disorders and intellectual disability. However, it is unclear whether this implicates the same specific variants, or even variants with the same functional effects on shared risk genes. Here, we show that de novo mutations in schizophrenia are generally of the same functional category as those that confer risk for NDDs, and that the specific de novo mutations in NDDs are enriched in schizophrenia. These findings indicate that, in part, NDDs and schizophrenia have shared molecular aetiology, and therefore likely overlapping pathophysiology. We also observe pleiotropic effects for variants known to be pathogenic for several syndromic developmental disorders, suggesting that schizophrenia should be included among the phenotypes associated with these mutations. Collectively, our findings support the hypothesis that at least some forms of schizophrenia lie within a continuum of neurodevelopmental disorders.

scholarly journals PsyMuKB: A De Novo Variant Knowledge Base Integrating Transcriptional and Translational Information to Identify Isoform-specific Mutations in Developmental Disorders

2019 ◽  
Author(s):  
Guan Ning Lin ◽  
Sijia Guo ◽  
Xian Tan ◽  
Weidi Wang ◽  
Wei Qian ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Protein Interactions ◽  
Developmental Disorders ◽  
De Novo ◽  
Genetic Disorders ◽  
Expression Patterns ◽  
Protein Structures ◽  
Autism Spectrum ◽  
Functional Studies ◽  
The Impact

AbstractDe novo variants (DNVs) are one of the most significant contributors to severe early-onset genetic disorders such as autism spectrum disorder, intellectual disability, and other developmental and neuropsychiatric (DNP) disorders. Currently, a plethora of DNVs have being identified through the use of next-generation sequencing and much effort has been made to understand their impact at the gene level; however, there has been little exploration of the impact at the isoform level. The brain contains a high level of alternative splicing and regulation, and exhibits a more divergent splicing program than other tissues; therefore, it is crucial to explore variants at the transcriptional regulation level to better interpret the mechanisms underlying DNP disorders. To facilitate better usage and improve the isoform-level interpretation of variants, we developed the PsyMuKB (NeuroPsychiatric Mutation Knowledge Base), a knowledge base containing a comprehensive, carefully curated list of DNVs with transcriptional and translational annotations to enable identification of isoform-specific mutations. PsyMuKB allows a flexible search of genes or variants and provides both table-based descriptions and associated visualizations, such as expression, transcript genomic structures, protein interactions, and the mutation sites mapped on the protein structures. It also provides an easy-to-use web interface, allowing users to rapidly visualize the locations and characteristics of mutations and the expression patterns of the impacted genes and isoforms. PsyMuKB thus constitutes a valuable resource for identifying tissue-specific de novo mutations for further functional studies of related disorders. PsyMuKB is freely accessible at http://psymukb.net.

scholarly journals Rare schizophrenia risk variants are enriched in genes shared with neurodevelopmental disorders

2016 ◽  
Author(s):  
Tarjinder Singh ◽  
James T. R. Walters ◽  
Mandy Johnstone ◽  
David Curtis ◽  
Jaana Suvisaari ◽  
...  
Keyword(s):  
Neurodevelopmental Disorders ◽  
Developmental Disorders ◽  
De Novo ◽  
Copy Number Variant ◽  
Autism Spectrum ◽  
Risk Genes ◽  
Mrna Targets ◽  
Risk Variants ◽  
Autism Spectrum Disorder Risk ◽  
Significant Enrichment

AbstractBy meta-analyzing rare coding variants in whole-exome sequences of 4,264 schizophrenia cases and 9,343 controls, de novo mutations in 1,077 trios, and array-based copy number variant calls from 6,882 cases and 11,255 controls, we show that individuals with schizophrenia carry a significant burden of rare damaging variants in a subset of 3,230 “highly constrained” genes previously identified as having near-complete depletion of protein truncating variants. Furthermore, rare variant enrichment analyses demonstrate that this burden is concentrated in known autism spectrum disorder risk genes, genes diagnostic of severe developmental disorders, and the autism-implicated sets of promoter targets of CHD8, and mRNA targets of FMRP. We further show that schizophrenia patients with intellectual disability have a greater enrichment of rare damaging variants in highly constrained genes and developmental disorder genes, but that a weaker but significant enrichment exists throughout the larger schizophrenia population. Combined, our results demonstrate that schizophrenia risk loci of large effect across a range of variant types implicate a common set of genes shared with broader neurodevelopmental disorders, suggesting a path forward in identifying additional risk genes in psychiatric disorders and further supporting a neurodevelopmental etiology to the pathogenesis of schizophrenia.

scholarly journals Integrated gene analyses of de novo mutations from 46,612 trios with autism and developmental disorders

2021 ◽  
Author(s):  
Tianyun Wang ◽  
Chang Kim ◽  
Trygve E. Bakken ◽  
Madelyn A. Gillentine ◽  
Barbara Henning ◽  
...  
Keyword(s):  
Developmental Disorders ◽  
De Novo ◽  
Autism Spectrum ◽  
Mutational Bias ◽  
De Novo Mutations ◽  
Cell Nuclei ◽  
Genetic Studies ◽  
Risk Genes ◽  
Functional Studies ◽  
Disease Subtypes

ABSTRACTMost genetic studies consider autism spectrum disorder (ASD) and developmental disorder (DD) separately despite overwhelming comorbidity and shared genetic etiology. Here we analyzed de novo mutations (DNMs) from 15,560 ASD (6,557 are new) and 31,052 DD trios independently and combined as broader neurodevelopmental disorders (NDD) using three models. We identify 615 candidate genes (FDR 5%, 189 potentially novel) by one or more models, including 138 reaching exome-wide significance (p < 3.64e-07) in all models. We find no evidence for ASD-specific genes in contrast to 18 genes significantly enriched for DD. There are 53 genes show particular mutational-bias including enrichments for missense (n=41) or truncating DNM (n=12). We find 22 genes with evidence of sex-bias including five X chromosome genes also with significant female burden (DDX3X, MECP2, SMC1A, WDR45, and HDAC8). NDD risk genes group into five functional networks associating with different brain developmental lineages based on single-cell nuclei transcriptomic data, which provides important insights into disease subtypes and future functional studies.

scholarly journals Identification of Autism Risk Genes in a Chinese Cohort via Whole-Exome Sequencing with the Joint Calling Analysis

2020 ◽  
Author(s):  
Bo Yuan ◽  
Peipei Cheng ◽  
Ran Zhang ◽  
Yasong Du ◽  
Zilong Qiu
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
De Novo ◽  
East Asian ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Large Sample Size ◽  
Risk Genes ◽  
Genetic Components ◽  
Whole Exome

Abstract Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder characterized by deficits in social interactions and repetitive behaviors. Although hundreds of ASD risk genes, implicated in synaptic formation, transcriptional regulation, and chromatin remodeling, have been identified, the genetic analysis on east Asian ASD cohorts in the whole-geome or whole-exome level is still limited(1-5). Here we performed whole-exome sequencing on 168 ASD probands with their unaffected parents of Chinese origin. We applied a joint calling analytical pipeline based on GATK best practices and identified numerous de novo variants including single nucleotide variants (SNVs) and insertion or deletions (INDELs). By querying the Simons foundation autism research initiative (SFARI) gene database, we found that there were potential novel ASD risk genes in East Asian cohorts, which did not exist in European American populations. Furthermore, our analysis pipeline identified de novo copy number variations (CNVs) of known ASD-related gene based on a sufficiently large sample size, validated by quantitative PCR. Our work indicated that there may be differences in potential ASD genetic components existing across different geographical populations, suggesting that genomic analysis over large cohorts are required for each population in order to precisely identify ASD risk genes.

scholarly journals A machine learning approach to predicting autism risk genes: Validation of known genes and discovery of new candidates

2018 ◽  
Author(s):  
Ying Lin ◽  
Anjali M. Rajadhyaksha ◽  
James B. Potash ◽  
Shizhong Han
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Human Brain ◽  
Candidate Genes ◽  
De Novo ◽  
Expression Patterns ◽  
Autism Spectrum ◽  
Gene Expression Patterns ◽  
Risk Genes ◽  
Gene Level

AbstractAutism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role ofde novomutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be differentially expressed in ASD brains, especially in the frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example,TCF20andFBOX11), but also indicated potentially novel candidates, such asDOCK3,MYCBP2andCAND1, which are all involved in neuronal development. Through extensive validations, we also showed that our method outperformed state-of-the-art scoring systems for ranking ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.

scholarly journals Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism

2018 ◽  
Author(s):  
F. Kyle Satterstrom ◽  
Jack A. Kosmicki ◽  
Jiebiao Wang ◽  
Michael S. Breen ◽  
Silvia De Rubeis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Exome Sequencing ◽  
Large Scale ◽  
De Novo ◽  
Regulation Of Gene Expression ◽  
Copy Number Variants ◽  
Autism Spectrum ◽  
Risk Genes ◽  
Functional Changes ◽  
Multiple Paths

SummaryWe present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n=35,584 total samples, 11,986 with ASD). Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate ≤ 0.1. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 53 show higher frequencies in individuals ascertained for ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most of the risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In human cortex single-cell gene expression data, expression of risk genes is enriched in both excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.

scholarly journals Dissecting Autism Genetic Risk Using Single-cell RNA-seq Data

2020 ◽  
Author(s):  
Siying Chen ◽  
Xueya Zhou ◽  
Eve Byington ◽  
Samuel L. Bruce ◽  
Haicang Zhang ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Sequencing ◽  
De Novo ◽  
Expression Patterns ◽  
Autism Spectrum ◽  
Supervised Machine Learning ◽  
Specific Gene ◽  
Rna Seq ◽  
Risk Genes ◽  
Sequencing Studies

AbstractAutism spectrum disorder (autism) is a condition with strong but heterogenous genetic contribution. Recent exome and genome sequencing studies have uncovered many new risk genes through de novo variants. However, a large fraction of enrichment of de novo variants observed in cases are not accounted for by known or candidate risk genes, suggesting that the majority of risk genes are still unknown. Here we hypothesize that autism risk genes share a few common cell-type specific gene expression patterns during brain development, and such information can be quantified to improve statistical power of detecting new risk genes. We obtained large-scale single-cell RNA-seq data from human fetal brain collected through a range of developmental stages, and developed a supervised machine-learning approach “A-risk” (Autism risk), to predict the plausibility of autism risk genes across the genome. Using data from recent exome sequencing studies of autism, A-risk achieves better performance in prioritizing de novo variants than other methods, especially for genes that are less intolerant of loss of function variants. We stratified genes based on A-risk and mutation intolerance metrics to improve estimation of priors in extTADA and identified 71 candidate risk genes. In particular, CLCN4, PRKAR1B, and NR2F1 are potentially new risk genes with further support from neurodevelopmental disorders. Expression patterns of both known and candidate risk genes reveals the important role of deep-layer excitatory neurons from adult human cortex in autism etiology. With the unprecedented revolution of single-cell transcriptomics and expanding autism cohorts with exome or genome sequencing, our method will facilitate systematic discovery of novel risk genes and understanding of biological pathogenesis in autism.

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