scholarly journals Exploring the Contribution to ADHD of Genes Involved in Mendelian Disorders Presenting with Hyperactivity and/or Inattention

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 93
Author(s):  
Noèlia Fernàndez-Castillo ◽  
Judit Cabana-Domínguez ◽  
Djenifer B. Kappel ◽  
Bàrbara Torrico ◽  
Heike Weber ◽  
...  
Keyword(s):  
Rare Variants ◽  
Neurodevelopmental Disorder ◽  
Gene List ◽  
Autism Spectrum ◽  
Mendelian Inheritance ◽  
Common Variants ◽  
Rare Disorders ◽  
Risk Genes ◽  
Complex Disorders ◽  
Mendelian Disorders

Attention-deficit hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder characterized by hyperactivity, impulsivity, and/or inattention, which are symptoms also observed in many rare genetic disorders. We searched for genes involved in Mendelian disorders presenting with ADHD symptoms in the Online Mendelian Inheritance in Man (OMIM) database, to curate a list of new candidate risk genes for ADHD. We explored the enrichment of functions and pathways in this gene list, and tested whether rare or common variants in these genes are associated with ADHD or with its comorbidities. We identified 139 genes, causal for 137 rare disorders, mainly related to neurodevelopmental and brain function. Most of these Mendelian disorders also present with other psychiatric traits that are often comorbid with ADHD. Using whole exome sequencing (WES) data from 668 ADHD cases, we found rare variants associated with the dimension of the severity of inattention symptoms in three genes: KIF11, WAC, and CRBN. Then, we focused on common variants and identified six genes associated with ADHD (in 19,099 cases and 34,194 controls): MANBA, UQCC2, HIVEP2, FOPX1, KANSL1, and AUH. Furthermore, HIVEP2, FOXP1, and KANSL1 were nominally associated with autism spectrum disorder (ASD) (18,382 cases and 27,969 controls), as well as HIVEP2 with anxiety (7016 cases and 14,475 controls), and FOXP1 with aggression (18,988 individuals), which is in line with the symptomatology of the rare disorders they are responsible for. In conclusion, inspecting Mendelian disorders and the genes responsible for them constitutes a valuable approach for identifying new risk genes and the mechanisms of complex disorders.

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.

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 Integrating de novo and inherited variants in over 42,607 autism cases identifies mutations in new moderate risk genes

2021 ◽  
Author(s):  
Xueya Zhou ◽  
Pamela Feliciano ◽  
Tianyun Wang ◽  
Irina Astrovskaya ◽  
Chang Shu ◽  
...  
Keyword(s):  
Genetic Risk ◽  
De Novo ◽  
Meta Analysis ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Loss Of Function ◽  
Moderate Risk ◽  
Full Spectrum ◽  
Risk Genes ◽  
Biological Parents

AbstractDespite the known heritable nature of autism spectrum disorder (ASD), studies have primarily identified risk genes with de novo variants (DNVs). To capture the full spectrum of ASD genetic risk, we performed a two-stage analysis of rare de novo and inherited coding variants in 42,607 ASD cases, including 35,130 new cases recruited online by SPARK. In the first stage, we analyzed 19,843 cases with one or both biological parents and found that known ASD or neurodevelopmental disorder (NDD) risk genes explain nearly 70% of the genetic burden conferred by DNVs. In contrast, less than 20% of genetic risk conferred by rare inherited loss-of-function (LoF) variants are explained by known ASD/NDD genes. We selected 404 genes based on the first stage of analysis and performed a meta-analysis with an additional 22,764 cases and 236,000 population controls. We identified 60 genes with exome-wide significance (p < 2.5e-6), including five new risk genes (NAV3, ITSN1, MARK2, SCAF1, and HNRNPUL2). The association of NAV3 with ASD risk is entirely driven by rare inherited LoFs variants, with an average relative risk of 4, consistent with moderate effect. ASD individuals with LoF variants in the four moderate risk genes (NAV3, ITSN1, SCAF1, and HNRNPUL2, n = 95) have less cognitive impairment compared to 129 ASD individuals with LoF variants in well-established, highly penetrant ASD risk genes (CHD8, SCN2A, ADNP, FOXP1, SHANK3) (59% vs. 88%, p= 1.9e-06). These findings will guide future gene discovery efforts and suggest that much larger numbers of ASD cases and controls are needed to identify additional genes that confer moderate risk of ASD through rare, inherited variants.

scholarly journals Linking Autism Risk Genes to Disruption of Cortical Development

Cells ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 2500
Author(s):  
Marta Garcia-Forn ◽  
Andrea Boitnott ◽  
Zeynep Akpinar ◽  
Silvia De Rubeis
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Neurodevelopmental Disorder ◽  
Cortical Development ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Genome Wide Association Studies ◽  
Restricted Interests ◽  
Risk Genes ◽  
Whole Exome

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder characterized by impairments in social communication and social interaction, and the presence of repetitive behaviors and/or restricted interests. In the past few years, large-scale whole-exome sequencing and genome-wide association studies have made enormous progress in our understanding of the genetic risk architecture of ASD. While showing a complex and heterogeneous landscape, these studies have led to the identification of genetic loci associated with ASD risk. The intersection of genetic and transcriptomic analyses have also begun to shed light on functional convergences between risk genes, with the mid-fetal development of the cerebral cortex emerging as a critical nexus for ASD. In this review, we provide a concise summary of the latest genetic discoveries on ASD. We then discuss the studies in postmortem tissues, stem cell models, and rodent models that implicate recently identified ASD risk genes in cortical development.

Neurobiology of Autism and Intellectual Disability

2017 ◽  
Author(s):  
Dejan B. Budimirovic ◽  
Megha Subramanian
Keyword(s):  
Fragile X ◽  
Neurodevelopmental Disorder ◽  
Full Range ◽  
Therapeutic Targets ◽  
Autism Spectrum ◽  
Fmr1 Gene ◽  
Health Concern ◽  
Complex Disorders ◽  
Mglur5 Antagonist ◽  
Core Symptoms

Fragile X syndrome (FXS) is a neurodevelopmental disorder that manifests with a range of cognitive, behavioral, and social impairments. It is a monogenetic disease caused by silencing of the FMR1 gene, in contrast to autism spectrum disorder (ASD) that is a behaviorally-defined set of complex disorders. Because ASD is a major and growing public health concern, current research is focused on identifying common therapeutic targets among patients with different molecular etiologies. Due to the prevalence of ASD in FXS and its shared neurophysiology with ASD, FXS has been extensively studied as a model for ASD. Studies in the animal models have provided breakthrough insights into the pathophysiology of FXS that have led to novel therapeutic targets for its core deficits (e.g., mGluR theory of fragile X). Yet recent clinical trials of both GABA-B agonist and mGluR5 antagonist revealed a lack of specific and sensitive outcome measures capturing the full range of improvements of patients with FXS. Recent research shows promise for the mapping of the multitude of genetic variants in ASD onto shared pathways with FXS. Nonetheless, in light of the huge level of locus heterogeneity in ASD, further effort in finding convergence in specific molecular pathways and reliable biomarkers is required in order to perform targeted treatment trials with sufficient sample size. This chapter focuses on the neurobehavioral phenotype caused by a full-mutation of the FMR1 gene, namely FXS, and the neurobiology of this disorder of relevance to the targeted molecular treatments of its core symptoms.

scholarly journals Exome sequencing identifies novel AD-associated genes.

2020 ◽  
Author(s):  
Henne Holstege ◽  
Marc Hulsman ◽  
Camille Charbonnier ◽  
Benjamin Grenier-Boley ◽  
Olivier Quenez ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Genetic Variants ◽  
Rare Variants ◽  
Mendelian Inheritance ◽  
Loss Of Function ◽  
App Processing ◽  
Complex Disorders ◽  
Pathogenic Variants ◽  
Increased Risk ◽  
The Impact

Background: With the development of next-generation sequencing technologies, it is possible to identify rare genetic variants that influence the risk of complex disorders. To date, whole exome sequencing (WES) strategies have shown that specific clusters of damaging rare variants in the TREM2, SORL1 and ABCA7 genes are associated with an increased risk of developing Alzheimers Disease (AD), reaching odds ratios comparable with the APOE-ε4 allele, the main common AD genetic risk factor. Here, we set out to identify additional AD-associated genes by an exome-wide investigation of the burden of rare damaging variants in the genomes of AD cases and cognitively healthy controls. Method: We integrated the data from 25,982 samples from the European ADES consortium and the American ADSP consortium. We developed new techniques to homogenise and analyse these data. Carriers of pathogenic variants in genes associated with Mendelian inheritance of dementia were excluded. After quality control, we used 12,652 AD cases and 8,693 controls for analysis. Genes were analysed using a burden analysis, including both non-synonymous and loss-of-function rare variants, the impact of which was prioritised using REVEL. Result: We confirmed that carrying rare protein-damaging genetic variants in TREM2, SORL1 or ABCA7 is associated with increased AD-risk. Moreover, we found that carrying rare damaging variants in the microglial ATP8B4 gene was significantly associated with AD, and we found suggestive evidence that rare variants in ADAM10, ABCA1, ORC6, B3GNT4 and SRC genes associated with increased AD risk. High-impact variants in these genes were mostly extremely rare and enriched in AD patients with earlier ages at onset. Additionally, we identified two suggestive protective associations in CBX3 and PRSS3. We are currently replicating these associations in independent datasets. Conclusion: With our newly developed homogenisation methods, we identified novel genetic determinants of AD which provide further evidence for a pivotal role of APP processing, lipid metabolism, and microglia and neuro-inflammatory processes in AD pathophysiology.

scholarly journals Novel Ultra-Rare Exonic Variants Identified in a Founder Population Implicate Cadherins in Schizophrenia

2020 ◽  
Author(s):  
Todd Lencz ◽  
Jin Yu ◽  
Raiyan Rashid Khan ◽  
Shai Carmi ◽  
Max Lam ◽  
...  
Keyword(s):  
Rare Variant ◽  
Rare Variants ◽  
Gene List ◽  
Ashkenazi Jewish ◽  
Founder Population ◽  
Total N ◽  
Gene Set ◽  
Complex Disorders ◽  
Gene Sets ◽  
Common Genetic Variants

AbstractIMPORTANCESchizophrenia is a serious mental illness with high heritability. While common genetic variants account for a portion of the heritability, identification of rare variants associated with the disorder has proven challenging.OBJECTIVETo identify genes and gene sets associated with schizophrenia in a founder population (Ashkenazi Jewish), and to determine the relative power of this population for rare variant discovery.DESIGN, SETTING, AND PARTICIPANTSData on exonic variants were extracted from whole genome sequences drawn from 786 patients with schizophrenia and 463 healthy control subjects, all drawn from the Ashkenazi Jewish population. Variants observed in two large publicly available datasets (total n≈153,000, excluding neuropsychiatric patients) were filtered out, and novel ultra-rare variants (URVs) were compared in cases and controls.MAIN OUTCOMES AND MEASURESThe number of novel URVs and genes carrying them were compared across cases and controls. Genes in which only cases or only controls carried novel, functional URVs were examined using gene set analyses.RESULTSCases had a higher frequency of novel missense or loss of function (MisLoF) variants compared to controls, as well as a greater number of genes impacted by MisLoF variants. Characterizing 141 “case-only” genes (in which ≥ 3 AJ cases in our dataset had MisLoF URVs with none found in our AJ controls), we replicated prior findings of both enrichment for synaptic gene sets, as well as specific genes such as SETD1A and TRIO. Additionally, we identified cadherins as a novel gene set associated with schizophrenia including a recurrent mutation in PCDHA3. Several genes associated with autism and other neurodevelopmental disorders including CACNA1E, ASXL3, SETBP1, and WDFY3, were also identified in our case-only gene list, as was TSC2, which is linked to tuberous sclerosis. Modeling the effects of purifying selection demonstrated that deleterious rare variants are greatly over-represented in a founder population with a tight bottleneck and rapidly expanding census, resulting in enhanced power for rare variant association studies.CONCLUSIONS AND RELEVANCEIdentification of cell adhesion genes in the cadherin/protocadherin family is consistent with evidence from large-scale GWAS in schizophrenia, helps specify the synaptic abnormalities that may be central to the disorder, and suggests novel potential treatment strategies (e.g., inhibition of protein kinase C). Study of founder populations may serve as a cost-effective way to rapidly increase gene discovery in schizophrenia and other complex disorders.

scholarly journals Brain-Specific Deletion of GIT1 Impairs Cognition and Alters Phosphorylation of Synaptic Protein Networks Implicated in Schizophrenia Susceptibility

2018 ◽  
Author(s):  
Daniel M. Fass ◽  
Michael C. Lewis ◽  
Rushdy Ahmad ◽  
Matthew J. Szucs ◽  
Qiangge Zhang ◽  
...  
Keyword(s):  
Cognitive Deficits ◽  
Neurodevelopmental Disorders ◽  
Rare Variants ◽  
Neurodevelopmental Disorder ◽  
Copy Number Variants ◽  
Synaptic Proteins ◽  
Interacting Protein ◽  
Risk Genes ◽  
Multiple Loci ◽  
Coding Variants

AbstractDespite tremendous effort, the molecular and cellular basis of cognitive deficits in schizophrenia remain poorly understood. Recent progress in elucidating the genetic architecture of schizophrenia has highlighted the association of multiple loci and rare variants that may impact susceptibility. One key example, given their potential etiopathogenic and therapeutic relevance, is a set of genes that encode proteins that regulate excitatory glutamatergic synapses in brain. A critical next step is to delineate specifically how such genetic variation impacts synaptic plasticity and to determine if and how the encoded proteins interact biochemically with one another to control cognitive function in a convergent manner. Towards this goal, here we study the roles of GPCR-kinase interacting protein 1 (GIT1), a synaptic scaffolding and signaling protein with damaging coding variants found in schizophrenia patients, as well as copy number variants found in patients with neurodevelopmental disorders. We generated conditional neural-selective GIT1 knockout mice and find that these mice have deficits in fear conditioning learning and spatial memory. Using global quantitative phospho-proteomics, we revealed that GIT1 deletion in brain perturbs specific networks of GIT1-interacting synaptic proteins. Importantly, several schizophrenia and neurodevelopmental disorder risk genes are present within these networks. We propose that GIT1 regulates the phosphorylation of a network of synaptic proteins and other critical regulators of neuroplasticity, and that perturbation of these networks may contribute to cognitive deficits observed in schizophrenia and neurodevelopmental disorders.

scholarly journals Detection of rare disease-related genetic variants using the birthday model

2018 ◽  
Author(s):  
Yael Berstein ◽  
Shane E. McCarthy ◽  
Melissa Kramer ◽  
W. Richard McCombie
Keyword(s):  
Bipolar Disorder ◽  
Exome Sequencing ◽  
Case Control Study ◽  
Rare Variants ◽  
Autism Spectrum ◽  
Case Control ◽  
Supplementary Information ◽  
Complex Disorders ◽  
Link Type ◽  
Control Study

AbstractMotivationExome sequencing is a powerful technique for the identification of disease-causing genes. A number of Mendelian inherited disease genes have been identified through this method. However, it remains a challenge to leverage exome sequencing for the study of complex disorders, such as schizophrenia and bipolar disorder, due to the genetic and phenotypic heterogeneity of these disorders. Although not feasible for many studies, sequencing large sample sizes (>10,000) may improve statistical power to associate more variants, while the aggregation of distinct rare variants associated with a given disease can make the identification of causal genes statistically challenging. Therefore, new methods for rare variant association are imperative to identify causative genes of complex disorders.ResultsHere we propose a method to predict causative rare variants using a popular probabilistic problem: The Birthday Model, which estimates the probability that multiple individuals in a group share the same birthday. We consider the probability and coincidence of samples sharing a variant akin to the chance of individuals sharing the same birthday. We investigated the parameter effects of our model, providing guidelines for its use and interpretation of the results. Using published data on autism spectrum disorder, hypertriglyceridemia in addition to a current case-control study on bipolar disorder, we evaluated this probabilistic method to identify potential causative variants. Several genes in the top results of the case-control study were associated with autism spectrum and bipolar disorder. Given that the core probability based on the birthday model is very sensitive to low recurrence, the method successfully tests the association of rare variants, which generally do not provide enough signal in commonly used statistical tests. Importantly, the simplicity of the model allows quick interpretation of genomic data, enabling users to select gene candidates for further biological validation of specific mutations and downstream functional or other studies.Availabilityhttps://github.com/yberstein/Birthday-Alqorithmhttp://labshare.cshl.edu/shares/mccombielab/www-data/Birthday-Algorithm/[email protected] (or [email protected])Supplementary informationSupplementary data are available online.

scholarly journals Whole Genome Sequencing in Psychiatric Disorders: the WGSPD Consortium

2017 ◽  
Author(s):  
Stephan J. Sanders ◽  
Benjamin M. Neale ◽  
Hailiang Huang ◽  
Donna M. Werling ◽  
Joon-Yong An ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Psychiatric Disorders ◽  
Genome Sequencing ◽  
Rare Variants ◽  
Autism Spectrum ◽  
Whole Genome ◽  
Complex Disorders ◽  
Computationally Intensive ◽  
Insight Into

AbstractAs technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome, through pilot WGS projects, will be critical to determine which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The WGSPD consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.

Sign in / Sign up

Export Citation Format

Share Document