scholarly journals Synaptic and Gene Regulatory Mechanisms in Schizophrenia, Autism, and 22q11.2 CNV Mediated Risk for Neuropsychiatric Disorders

2019 ◽  
Author(s):  
Jennifer K. Forsyth ◽  
Daniel Nachun ◽  
Michael J. Gandal ◽  
Daniel H. Geschwind ◽  
Ariana E. Anderson ◽  
...  
Keyword(s):  
Gene Networks ◽  
Disease Risk ◽  
Neuropsychiatric Disorders ◽  
Autism Spectrum ◽  
Synaptic Function ◽  
Protein Interaction Data ◽  
Regulatory Pathways ◽  
Polygenic Risk ◽  
Risk Variants ◽  
Gene Regulatory

AbstractBackground22q11.2 copy number variants (CNVs) are among the most highly penetrant genetic risk variants for developmental neuropsychiatric disorders such as schizophrenia (SCZ) and autism spectrum disorder (ASD). However, the specific mechanisms through which they confer risk remain unclear.MethodsUsing a functional genomics approach, we integrated transcriptomic data from the developing human brain, genome-wide association findings for SCZ and ASD, protein interaction data, and pathophysiological signatures of SCZ and ASD to: 1) organize genes into the developmental cellular and molecular systems within which they operate; 2) identify neurodevelopmental processes associated with polygenic risk for SCZ and ASD across the allelic frequency spectrum; and 3) elucidate pathways and individual genes through which 22q11.2 CNVs may confer risk for each disorder.ResultsPolygenic risk for SCZ and ASD converged on partially overlapping gene networks involved in synaptic function and transcriptional regulation, with ASD risk variants additionally enriched for networks involved in neuronal differentiation during fetal development. The 22q11.2 locus formed a large protein network that disproportionately affected SCZ- and ASD-associated neurodevelopmental networks, including loading highly onto synaptic and gene regulatory pathways. SEPT5, PI4KA, and SNAP29 genes are candidate drivers of 22q11.2 synaptic pathology relevant to SCZ and ASD, and DGCR8 and HIRA are candidate drivers of disease-relevant alterations in gene regulation.ConclusionsThe current approach provides a powerful framework to identify neurodevelopmental processes affected by diverse risk variants for SCZ and ASD, and elucidate the mechanisms through which highly penetrant multi-gene CNVs contribute to disease risk.

scholarly journals Cardiac Cell Type-Specific Gene Regulatory Programs and Disease Risk Association

2020 ◽  
Author(s):  
James D. Hocker ◽  
Olivier B. Poirion ◽  
Fugui Zhu ◽  
Justin Buchanan ◽  
Kai Zhang ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Cell Types ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Dependent Manner ◽  
Cardiac Cell ◽  
Risk Variants ◽  
Gene Regulatory

ABSTRACTBackgroundCis-regulatory elements such as enhancers and promoters are crucial for directing gene expression in the human heart. Dysregulation of these elements can result in many cardiovascular diseases that are major leading causes of morbidity and mortality worldwide. In addition, genetic variants associated with cardiovascular disease risk are enriched within cis-regulatory elements. However, the location and activity of these cis-regulatory elements in individual cardiac cell types remains to be fully defined.MethodsWe performed single nucleus ATAC-seq and single nucleus RNA-seq to define a comprehensive catalogue of candidate cis-regulatory elements (cCREs) and gene expression patterns for the distinct cell types comprising each chamber of four non-failing human hearts. We used this catalogue to computationally deconvolute dynamic enhancers in failing hearts and to assign cardiovascular disease risk variants to cCREs in individual cardiac cell types. Finally, we applied reporter assays, genome editing and electrophysiogical measurements in in vitro differentiated human cardiomyocytes to validate the molecular mechanisms of cardiovascular disease risk variants.ResultsWe defined >287,000 candidate cis-regulatory elements (cCREs) in human hearts at single-cell resolution, which notably revealed gene regulatory programs controlling specific cell types in a cardiac region/structure-dependent manner and during heart failure. We further report enrichment of cardiovascular disease risk variants in cCREs of distinct cardiac cell types, including a strong enrichment of atrial fibrillation variants in cardiomyocyte cCREs, and reveal 38 candidate causal atrial fibrillation variants localized to cardiomyocyte cCREs. Two such risk variants residing within a cardiomyocyte-specific cCRE at the KCNH2/HERG locus resulted in reduced enhancer activity compared to the non-risk allele. Finally, we found that deletion of the cCRE containing these variants decreased KCNH2 expression and prolonged action potential repolarization in an enhancer dosage-dependent manner.ConclusionsThis comprehensive atlas of human cardiac cCREs provides the foundation for not only illuminating cell type-specific gene regulatory programs controlling human hearts during health and disease, but also interpreting genetic risk loci for a wide spectrum of cardiovascular diseases.

scholarly journals Common genetic risk variants identified in the SPARK cohort implicate DDHD2 as a novel autism risk gene

2020 ◽  
Author(s):  
Nana Matoba ◽  
Dan Liang ◽  
Huaigu Sun ◽  
Nil Aygün ◽  
Jessica C. McAfee ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Association Study ◽  
Genetic Risk ◽  
Molecular Mechanisms ◽  
Autism Spectrum ◽  
Risk Variants ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
A Genome ◽  
Gene Regulatory

AbstractBackgroundAutism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder. Large genetically informative cohorts of individuals with ASD have led to the identification of three common genome-wide significant (GWS) risk loci to date. However, many more common genetic variants are expected to contribute to ASD risk given the high heritability. Here, we performed a genome-wide association study (GWAS) using the Simons Foundation Powering Autism Research for Knowledge (SPARK) dataset to identify additional common genetic risk factors and molecular mechanisms underlying risk for ASD.MethodsWe performed an association study on 6,222 case-pseudocontrol pairs from SPARK and meta-analyzed with a previous GWAS. We integrated gene regulatory annotations to map non-coding risk variants to their regulated genes. Further, we performed a massively parallel reporter assay (MPRA) to identify causal variant(s) within a novel risk locus.ResultsWe identified one novel GWS locus from the SPARK GWAS. The meta-analysis identified four significant loci, including an additional novel locus. We observed significant enrichment of ASD heritability within regulatory regions of the developing cortex, indicating that disruption of gene regulation during neurodevelopment is critical for ASD risk. The MPRA identified one variant at the novel locus with strong impacts on gene regulation (rs7001340), and expression quantitative trait loci data demonstrated an association between the risk allele and decreased expression of DDHD2 (DDHD domain containing 2) in both adult and pre-natal brains.ConclusionsBy integrating genetic association data with multi-omic gene regulatory annotations and experimental validation, we fine-mapped a causal risk variant and demonstrated that DDHD2 is a novel gene associated with ASD risk.

scholarly journals Autism spectrum disorder at the crossroad between genes and environment: contributions, convergences, and interactions in ASD developmental pathophysiology

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Cristina Cheroni ◽  
Nicolò Caporale ◽  
Giuseppe Testa
Keyword(s):  
Autism Spectrum Disorder ◽  
Environmental Factors ◽  
Neurodevelopmental Disorders ◽  
Chemical Exposure ◽  
Autism Spectrum ◽  
Synaptic Function ◽  
Spectrum Disorder ◽  
Regulatory Pathways ◽  
Genes And Environment ◽  
Analytical Approaches

Abstract The complex pathophysiology of autism spectrum disorder encompasses interactions between genetic and environmental factors. On the one hand, hundreds of genes, converging at the functional level on selective biological domains such as epigenetic regulation and synaptic function, have been identified to be either causative or risk factors of autism. On the other hand, exposure to chemicals that are widespread in the environment, such as endocrine disruptors, has been associated with adverse effects on human health, including neurodevelopmental disorders. Interestingly, experimental results suggest an overlap in the regulatory pathways perturbed by genetic mutations and environmental factors, depicting convergences and complex interplays between genetic susceptibility and toxic insults. The pervasive nature of chemical exposure poses pivotal challenges for neurotoxicological studies, regulatory agencies, and policy makers. This highlights an emerging need of developing new integrative models, including biomonitoring, epidemiology, experimental, and computational tools, able to capture real-life scenarios encompassing the interaction between chronic exposure to mixture of substances and individuals’ genetic backgrounds. In this review, we address the intertwined roles of genetic lesions and environmental insults. Specifically, we outline the transformative potential of stem cell models, coupled with omics analytical approaches at increasingly single cell resolution, as converging tools to experimentally dissect the pathogenic mechanisms underlying neurodevelopmental disorders, as well as to improve developmental neurotoxicology risk assessment.

scholarly journals Impute.me: an open source, non-profit tool for using data from DTC genetic testing to calculate and interpret polygenic risk scores

2019 ◽  
Author(s):  
Lasse Folkersen ◽  
Oliver Pain ◽  
Andres Ingasson ◽  
Thomas Werge ◽  
Cathryn M. Lewis ◽  
...  
Keyword(s):  
Open Source ◽  
Public Perception ◽  
Disease Risk ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Complex Disorders ◽  
Risk Variants ◽  
Genome Wide ◽  
The Impact

AbstractTo date, interpretation of genomic information has focused on single variants conferring disease risk, but most disorders of major public concern have a polygenic architecture. Polygenic risk scores (PRS) give a single measure of disease liability by summarising disease risk across hundreds of thousands of genetic variants. They can be calculated in any genome-wide genotype data-source, using a prediction model based on genome-wide summary statistics from external studies.As genome-wide association studies increase in power, the predictive ability for disease risk will also increase. While PRS are unlikely ever to be fully diagnostic, they may give valuable medical information for risk stratification, prognosis, or treatment response prediction.Public engagement is therefore becoming important on the potential use and acceptability of PRS. However, the current public perception of genetics is that it provides ‘Yes/No’ answers about the presence/absence of a condition, or the potential for developing a condition, which in not the case for common, complex disorders with of polygenic architecture.Meanwhile, unregulated third-party applications are being developed to satisfy consumer demand for information on the impact of lower risk variants on common diseases that are highly polygenic. Often applications report results from single SNPs and disregard effect size, which is highly inappropriate for common, complex disorders where everybody carries risk variants.Tools are therefore needed to communicate our understanding of genetic predisposition as a continuous trait, where a genetic liability confers risk for disease. Impute.me is one such a tool, whose focus is on education and information on common, complex disorders with polygenetic architecture. Its research-focused open-source website allows users to upload consumer genetics data to obtain PRS, with results reported on a population-level normal distribution. Diseases can only be browsed by ICD10-chapter-location or alphabetically, thus prompting the user to consider genetic risk scores in a medical context of relevance to the individual.Here we present an overview of the implementation of the impute.me site, along with analysis of typical usage-patterns, which may advance public perception of genomic risk and precision medicine.

scholarly journals Global gene network exploration based on explainable artificial intelligence approach

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241508
Author(s):  
Heewon Park ◽  
Koji Maruhashi ◽  
Rui Yamaguchi ◽  
Seiya Imoto ◽  
Satoru Miyano
Keyword(s):  
Artificial Intelligence ◽  
Network Analysis ◽  
Gene Networks ◽  
Gene Network ◽  
Multilayer Networks ◽  
Mesenchymal Transition ◽  
Regulatory Pathways ◽  
Regulatory Systems ◽  
Explainable Ai ◽  
Gene Regulatory

In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statistical and machine learning approaches have been developed and applied to reveal sample-specific regulatory pathways, integrative understanding of the massive multilayer networks remains a challenge. To resolve this problem, we propose a novel artificial intelligence (AI) strategy for comprehensive gene regulatory network analysis. In our strategy, personalized gene networks corresponding specific clinical characteristic are constructed and the constructed network is considered as a second-order tensor. Then, an explainable AI method based on deep learning is applied to decompose the multilayer networks, thus we can reveal all-encompassing gene regulatory systems characterized by clinical features of patients. To evaluate the proposed methodology, we apply our method to the multilayer gene networks under varying conditions of an epithelial–mesenchymal transition (EMT) process. From the comprehensive analysis of multilayer networks, we identified novel markers, and the biological mechanisms of the identified genes and their reciprocal mechanisms are verified through the literature. Although any biological knowledge about the identified genes was not incorporated in our analysis, our data-driven approach based on AI approach provides biologically reliable results. Furthermore, the results provide crucial evidences to reveal biological mechanism related to various diseases, e.g., keratinocyte proliferation. The use of explainable AI method based on the tensor decomposition enables us to reveal global and novel mechanisms of gene regulatory system from the massive multiple networks, which cannot be demonstrated by existing methods. We expect that the proposed method provides a new insight into network biology and it will be a useful tool to integrative gene network analysis related complex architectures of diseases.

scholarly journals Schizophrenia hiPSC neurons display expression changes that are enriched for disease risk variants and a blunted activity-dependent response

2016 ◽  
Author(s):  
Panos Roussos ◽  
Boris Guennewig ◽  
Dominik C. Kaczorowski ◽  
Guy Barry ◽  
Kristen J. Brennand
Keyword(s):  
Gene Expression ◽  
Candidate Genes ◽  
Expression Analysis ◽  
Gene Networks ◽  
Disease Risk ◽  
Fragile X ◽  
Differential Expression Analysis ◽  
Therapeutic Interventions ◽  
Risk Variants ◽  
Activity Dependent

ABSTRACTIMPORTANCESchizophrenia (SCZ) is a common illness with complex genetic architecture where both common genetic variation and rare mutations have been implicated. SCZ candidate genes participate in common molecular pathways that are regulated by activity-dependent changes in neurons, including the signaling network that modulates synaptic strength and the network of genes that are targets of fragile X mental retardation protein. One important next step is to further our understanding on the role of activity-dependent changes of genes expression in the etiopathogenesis of SCZ.OBJECTIVETo examine whether neuronal activity-dependent changes of gene expression is dysregulated in SCZ.DESIGN, SETTING, AND PARTICIPANTSNeurons differentiated from human induced pluripotent stem cells (hiPSCs) derived from 4 cases with SCZ and 4 unaffected controls were depolarized using potassium chloride. RNA was extracted followed by genome-wide profiling of the transcriptome.MAIN OUTCOMES AND MEASURESWe performed differential expression analysis and gene co-expression analysis to identify activity-dependent or disease-specific changes of the transcriptome. Further, we used gene set analyses to identify co-expressed modules that are enriched for SCZ risk genes.RESULTSWe identified 1,669 genes that are significantly different in SCZ-associated vs. control hiPSC-derived neurons and 1,199 genes that are altered in these cells in response to depolarization. We show that the effect of activity-dependent changes of gene expression in SCZ-associated neurons is attenuated compared to controls. Furthermore, these differentially expressed genes are co-expressed in modules that are highly enriched for genes affected by genetic risk variants in SCZ and other neurodevelopmental disorders.CONCLUSIONS AND RELEVANCEOur results show that SCZ candidate genes converge to gene networks that are associated with a blunted effect of activity-dependent changes of gene expression in SCZ-associated neurons. Overall, these findings show that hiPSC neurons demonstrate activity-dependent transcriptional changes that can be utilized to examine underlying mechanisms and therapeutic interventions related to SCZ.

scholarly journals Integrative analysis of multi-omics reveals gene regulatory networks across brain regions from risk variants to phenotypes of Alzheimer’s disease and Covid-19

2021 ◽  
Author(s):  
Saniya Khullar ◽  
Daifeng Wang
Keyword(s):  
Disease Progression ◽  
Temporal Lobe ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Disease Risk ◽  
Regulatory Elements ◽  
Brain Regions ◽  
Risk Variants ◽  
Gene Regulatory

AbstractBackgroundGenome-wide association studies have found many genetic risk variants associated with Alzheimer’s disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed integrative multi-omics analysis from genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes.MethodFirst, we cluster gene co-expression networks and identify gene modules for various AD phenotypes given population gene expression data. Next, we predict the transcription factors (TFs) that significantly regulate the genes in each module and the AD risk variants (e.g., SNPs) interrupting the TF binding sites on the regulatory elements. Finally, we construct a full gene regulatory network linking SNPs, interrupted TFs, and regulatory elements to target genes for each phenotype. This network thus provides mechanistic insights of gene regulation from disease risk variants to AD phenotypes.ResultsWe applied our analysis to predict the gene regulatory networks in three major AD-relevant regions: hippocampus, dorsolateral prefrontal cortex (DLPFC), and lateral temporal lobe (LTL). These region networks provide a comprehensive functional genomic map linking AD SNPs to TFs and regulatory elements to target genes for various AD phenotypes. Comparative analyses further revealed cross-region-conserved and region-specific regulatory networks. For instance, AD SNPs rs13404184 and rs61068452 disrupt the bindings of TF SPI1 that regulates AD gene INPP5D in the hippocampus and lateral temporal lobe. However, SNP rs117863556 interrupts the bindings of TF REST to regulate GAB2 in the DLPFC only. Furthermore, driven by recent discoveries between AD and Covid-19, we found that many genes from our networks regulating Covid-19 pathways are also significantly differentially expressed in severe Covid patients (ICU), suggesting potential regulatory connections between AD and Covid. Thus, we used the machine learning models to predict severe Covid and prioritized highly predictive genes as AD-Covid genes. We also used Decision Curve Analysis to show that our AD-Covid genes outperform known Covid-19 genes for predicting Covid severity and deciding to send patients to ICU or not. In short, our results provide a deeper understanding of the interplay among multi-omics, brain regions, and AD phenotypes, including disease progression and Covid response. Our analysis is open-source available at https://github.com/daifengwanglab/ADSNPheno.

scholarly journals A multi-enhancer RET regulatory code is disrupted in Hirschsprung disease

2020 ◽  
Author(s):  
Sumantra Chatterjee ◽  
Kameko M Karasaki ◽  
Ashish Kapoor ◽  
Aravinda Chakravarti
Keyword(s):  
Gene Expression ◽  
Complex Disease ◽  
Disease Risk ◽  
Hirschsprung Disease ◽  
Kinase Gene ◽  
Risk Variants ◽  
Its Gene ◽  
Tyrosine Kinase Gene ◽  
Receptor Tyrosine Kinase Gene ◽  
Gene Regulatory

AbstractIn Hirschsprung disease (HSCR; congenital colonic aganglionosis), three GWAS-identified common variants residing in three distinct enhancers of the RET receptor tyrosine kinase gene reduce its gene expression and are causal disease risk variants. Further, their combined effects significantly dysregulate the expression of other functionally-related genes defining the RET gene regulatory network (GRN). In this study, we asked how many variants in how many distinct RET enhancers affect HSCR risk by reducing RET gene expression? We demonstrate that 22 additional HSCR-associated polymorphisms, both independent and associated, reside within multiple candidate RET enhancers and among which 7 display differential allelic enhancer activities. Of these 7 RET enhancers, two bind PAX3 and extend the known RET-EDNRB GRN. Therefore, sequence variants within a minimum of 10 RET enhancers affect HSCR risk, revealing a diverse regulatory code modulating complex disease risk even at a single locus.

scholarly journals The architecture of brain co-expression reveals the brain-wide basis of disease susceptibility

2020 ◽  
Author(s):  
CL Hartl ◽  
G Ramaswami ◽  
WG Pembroke ◽  
S Muller ◽  
G Pintacuda ◽  
...  
Keyword(s):  
Gene Networks ◽  
Regulatory Networks ◽  
Disease Risk ◽  
Cell Types ◽  
Brain Regions ◽  
Specific Cell ◽  
Risk Variants ◽  
Normal Individuals ◽  
Broad Array ◽  
The Brain

AbstractGene networks have proven their utility for elucidating transcriptome structure in the brain, yielding numerous biological insights. Most analyses have focused on expression relationships within a circumspect number of regions – how these relationships vary across a broad array of brain regions is largely unknown. By leveraging RNA-sequencing in 864 samples representing 12 brain regions in a cohort of 131 phenotypically normal individuals, we identify 12 brain-wide, 114 region-specific, and 50 cross-regional co-expression modules. We replicate the majority (81%) of modules in regional microarray datasets. Nearly 40% of expressed genes fall into brain-wide modules corresponding to major cell classes and conserved biological processes. Region-specific modules comprise 25% of expressed genes and correspond to region-specific cell types and processes, such as oxytocin signaling in the hypothalamus, or addiction pathways in the nucleus accumbens. We further leverage these modules to capture cell-type-specific lncRNA and gene isoforms, both of which contribute substantially to regional synaptic diversity. We identify enrichment of neuropsychiatric disease risk variants in brain wide and multi-regional modules, consistent with their broad impact on cell classes, and highlight specific roles in neuronal proliferation and activity-dependent processes. Finally, we examine the manner in which gene co-expression and gene regulatory networks reflect genetic risk, including the recently framed omnigenic model of disease architecture.

scholarly journals Role of Common Genetic Variants for Drug-Resistance to Specific Anti-Seizure Medications

2021 ◽  
Vol 12 ◽  
Author(s):  
Stefan Wolking ◽  
Ciarán Campbell ◽  
Caragh Stapleton ◽  
Mark McCormack ◽  
Norman Delanty ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Genetic Variants ◽  
Neuropsychiatric Disorders ◽  
Polygenic Risk Score ◽  
Specific Response ◽  
Association Analyses ◽  
Polygenic Risk ◽  
Risk Variants ◽  
Genome Wide ◽  
Common Genetic Variants

Objective: Resistance to anti-seizure medications (ASMs) presents a significant hurdle in the treatment of people with epilepsy. Genetic markers for resistance to individual ASMs could support clinicians to make better-informed choices for their patients. In this study, we aimed to elucidate whether the response to individual ASMs was associated with common genetic variation.Methods: A cohort of 3,649 individuals of European descent with epilepsy was deeply phenotyped and underwent single nucleotide polymorphism (SNP)-genotyping. We conducted genome-wide association analyses (GWASs) on responders to specific ASMs or groups of functionally related ASMs, using non-responders as controls. We performed a polygenic risk score (PRS) analyses based on risk variants for epilepsy and neuropsychiatric disorders and ASM resistance itself to delineate the polygenic burden of ASM-specific drug resistance.Results: We identified several potential regions of interest but did not detect genome-wide significant loci for ASM-specific response. We did not find polygenic risk for epilepsy, neuropsychiatric disorders, and drug-resistance associated with drug response to specific ASMs or mechanistically related groups of ASMs.Significance: This study could not ascertain the predictive value of common genetic variants for ASM responder status. The identified suggestive loci will need replication in future studies of a larger scale.

Sign in / Sign up

Export Citation Format

Share Document