Massively parallel functional dissection of schizophrenia associated non-coding genetic variants [ATAC-seq]
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE225816 | GEO | 2023/10/16
REPOSITORIES: GEO
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Project description:Schizophrenia (SCZ) is a highly polygenic disease and genome wide association studies have identified thousands of genetic variants that are statistically associated with this psychiatric disorder. However, our ability to translate these associations into insights on the disease mechanisms has been challenging since the causal genetic variants, their molecular function and their target genes remain largely unknown. In order to address these questions, we established a functional genomics pipeline in combination with induced pluripotent stem cell technology to functionally characterize 35,000 non-coding genetic variants associated with schizophrenia along with their target genes. This analysis identified a set of 620 (1.7%) single nucleotide polymorphisms as functional on a molecular level in a highly cell type and condition specific fashion. These results provide a high-resolution map of functional variant-gene combinations and offer comprehensive biological insights into the developmental context and stimulation dependent molecular processes modulated by SCZ associated genetic variation.
2023-10-16 | GSE225815 | GEO
Project description:Schizophrenia (SCZ) is a highly polygenic disease and genome wide association studies have identified thousands of genetic variants that are statistically associated with this psychiatric disorder. However, our ability to translate these associations into insights on the disease mechanisms has been challenging since the causal genetic variants, their molecular function and their target genes remain largely unknown. In order to address these questions, we established a functional genomics pipeline in combination with induced pluripotent stem cell technology to functionally characterize 35,000 non-coding genetic variants associated with schizophrenia along with their target genes. This analysis identified a set of 620 (1.7%) single nucleotide polymorphisms as functional on a molecular level in a highly cell type and condition specific fashion. These results provide a high-resolution map of functional variant-gene combinations and offer comprehensive biological insights into the developmental context and stimulation dependent molecular processes modulated by SCZ associated genetic variation.
2023-10-16 | GSE225814 | GEO
Project description:Schizophrenia (SCZ) is a highly polygenic disease and genome wide association studies have identified thousands of genetic variants that are statistically associated with this psychiatric disorder. However, our ability to translate these associations into insights on the disease mechanisms has been challenging since the causal genetic variants, their molecular function and their target genes remain largely unknown. In order to address these questions, we established a functional genomics pipeline in combination with induced pluripotent stem cell technology to functionally characterize 35,000 non-coding genetic variants associated with schizophrenia along with their target genes. This analysis identified a set of 620 (1.7%) single nucleotide polymorphisms as functional on a molecular level in a highly cell type and condition specific fashion. These results provide a high-resolution map of functional variant-gene combinations and offer comprehensive biological insights into the developmental context and stimulation dependent molecular processes modulated by SCZ associated genetic variation.
2023-10-16 | GSE225813 | GEO
Project description:Schizophrenia (SCZ) is a severe mental disorder affecting 1% of the world population. SCZ is characterized by an underlying genetic architecture that is highly polygenic. Genome wide association studies have identified thousands of genetic variants that are statistically linked to the disease. However, the translation of these associations into insights on the pathomechanisms has been challenging because the causal genetic variants, their molecular function, and their target genes remain largely unknown. To address these questions, we combined induced pluripotent stem cell technology with a massively parallel variant annotation pipeline (MVAP) to functionally characterize 35,000 SCZ associated non-coding genetic variants. This approach identified a set of 620 (1.7%) single nucleotide polymorphisms as functional on the molecular level in a highly cell type and condition specific fashion. Subsequent multi-modal integration of epigenomic data combined with CRISPR screening in human neurons enabled us to systematically translate SCZ variant associations into target genes, biological processes, and ultimately alterations of neuronal physiology. These results provide a new high-resolution map of functional variant-gene combinations and offer comprehensive biological insights into the developmental context and stimulus dependent molecular processes modulated by SCZ genetic variation beyond statistical association.
| EGAS00001007542 | EGA
Project description:Background: Schizophrenia is a severe, highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Insights into the functional complexity of the genome have focussed attention on the role of non-sequence-based genomic variation in health and disease. Although a better understanding of the molecular mechanisms underlying disease phenotypes is best achieved using an integrated functional genomics strategy, few studies have attempted to systematically integrate genetic and epigenetic epidemiological approaches. Results: We performed a multi-stage epigenome-wide association study (EWAS), quantifying genome-wide patterns of DNA methylation in a total of 1,801 individuals from three independent sample cohorts. We identified multiple differentially methylated positions (DMPs) and region (DMRs) associated with schizophrenia, independently of important confounders such as smoking, with consistent effects across the three independent cohorts. We also show that polygenic burden for schizophrenia is associated with epigenetic variation at multiple loci across the genome, independently of loci implicated in the analysis of diagnosed schizophrenia. Finally, we show how DNA methylation quantitative trait loci (mQTL) analyses can be used to annotate the extended genomic regions nominated by genetic studies of schizophrenia, with Bayesian co-localization analyses highlighting potential regulatory variation causally involved in disease. Conclusion: This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological pipeline that can be used to inform EWAS analyses of other complex traits and diseases. We demonstrate the utility of using polygenic risk score (PRS) for identifying molecular variation associated with etiological variation, and mQTLs for refining the functional/regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation. 675 whole blood derived DNA samples (353 schizophrenia cases and 322 controls) representing phase 1 of our meta-analysis. Bisulfite converted DNA from these samples were hybridized to the Illumina Infinium 450k Human Methylation Beadchip v1.0.
2016-08-01 | E-GEOD-80417 | biostudies-arrayexpress
Project description:Abstract Background: Schizophrenia is a severe, highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Insights into the functional complexity of the genome have focussed attention on the role of non-sequence-based genomic variation in health and disease. Although a better understanding of the molecular mechanisms underlying disease phenotypes is best achieved using an integrated functional genomics strategy, few studies have attempted to systematically integrate genetic and epigenetic epidemiological approaches. Results: We performed a multi-stage epigenome-wide association study (EWAS), quantifying genome-wide patterns of DNA methylation in a total of 1,801 individuals from three independent sample cohorts. We identified multiple differentially methylated positions (DMPs) and region (DMRs) associated with schizophrenia, independently of important confounders such as smoking, with consistent effects across the three independent cohorts. We also show that polygenic burden for schizophrenia is associated with epigenetic variation at multiple loci across the genome, independently of loci implicated in the analysis of diagnosed schizophrenia. Finally, we show how DNA methylation quantitative trait loci (mQTL) analyses can be used to annotate the extended genomic regions nominated by genetic studies of schizophrenia, with Bayesian co-localization analyses highlighting potential regulatory variation causally involved in disease. Conclusion: This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological pipeline that can be used to inform EWAS analyses of other complex traits and diseases. We demonstrate the utility of using polygenic risk score (PRS) for identifying molecular variation associated with etiological variation, and mQTLs for refining the functional/regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation. 847 whole blood derived DNA samples (414 schizophrenia cases and 433 controls) representing phase 2 of our meta-analysis. Bisulfite converted DNA from these samples were hybridised to the Illumina Infinium 450k Human Methylation Beadchip v1.0.
2016-08-01 | E-GEOD-84727 | biostudies-arrayexpress
Project description:Abstract Background: Schizophrenia is a severe, highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Insights into the functional complexity of the genome have focussed attention on the role of non-sequence-based genomic variation in health and disease. Although a better understanding of the molecular mechanisms underlying disease phenotypes is best achieved using an integrated functional genomics strategy, few studies have attempted to systematically integrate genetic and epigenetic epidemiological approaches. Results: We performed a multi-stage epigenome-wide association study (EWAS), quantifying genome-wide patterns of DNA methylation in a total of 1,801 individuals from three independent sample cohorts. We identified multiple differentially methylated positions (DMPs) and region (DMRs) associated with schizophrenia, independently of important confounders such as smoking, with consistent effects across the three independent cohorts. We also show that polygenic burden for schizophrenia is associated with epigenetic variation at multiple loci across the genome, independently of loci implicated in the analysis of diagnosed schizophrenia. Finally, we show how DNA methylation quantitative trait loci (mQTL) analyses can be used to annotate the extended genomic regions nominated by genetic studies of schizophrenia, with Bayesian co-localization analyses highlighting potential regulatory variation causally involved in disease. Conclusion: This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological pipeline that can be used to inform EWAS analyses of other complex traits and diseases. We demonstrate the utility of using polygenic risk score (PRS) for identifying molecular variation associated with etiological variation, and mQTLs for refining the functional/regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.
2016-08-01 | GSE84727 | GEO
Project description:Background: Schizophrenia is a severe, highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Insights into the functional complexity of the genome have focussed attention on the role of non-sequence-based genomic variation in health and disease. Although a better understanding of the molecular mechanisms underlying disease phenotypes is best achieved using an integrated functional genomics strategy, few studies have attempted to systematically integrate genetic and epigenetic epidemiological approaches. Results: We performed a multi-stage epigenome-wide association study (EWAS), quantifying genome-wide patterns of DNA methylation in a total of 1,801 individuals from three independent sample cohorts. We identified multiple differentially methylated positions (DMPs) and region (DMRs) associated with schizophrenia, independently of important confounders such as smoking, with consistent effects across the three independent cohorts. We also show that polygenic burden for schizophrenia is associated with epigenetic variation at multiple loci across the genome, independently of loci implicated in the analysis of diagnosed schizophrenia. Finally, we show how DNA methylation quantitative trait loci (mQTL) analyses can be used to annotate the extended genomic regions nominated by genetic studies of schizophrenia, with Bayesian co-localization analyses highlighting potential regulatory variation causally involved in disease. Conclusion: This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological pipeline that can be used to inform EWAS analyses of other complex traits and diseases. We demonstrate the utility of using polygenic risk score (PRS) for identifying molecular variation associated with etiological variation, and mQTLs for refining the functional/regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.
2016-08-01 | GSE80417 | GEO
Project description:Whole-genome expression studies in peripheral tissues of patients affected by schizophrenia (SCZ) can provide new insights into the molecular basis of the disorder and innovative biomarkers that may be of great usefulness in the clinical practice. Recent evidence suggests that skin fibroblasts could represent a non-neural peripheral model useful to investigate molecular alterations in psychiatric disorders. A microarray expression study was conducted comparing transcriptomic profiles of skin fibroblasts from SCZ patients and controls. Fibroblasts can be more advantageous to discover mental disorder aetiological mechanisms since they seem more similar to neurons and less affected by the environmental confounders. Transcriptomic profiles of human skin fibroblasts obtained from 20 schizophrenia patients were compared to 20 controls
2015-02-17 | E-GEOD-62333 | biostudies-arrayexpress
Project description:Genetic association studies provide evidence for a substantial polygenic component to schizophrenia, although the neurobiological mechanisms underlying the disorder remain largely undefined. Building on recent studies supporting a role for developmentally regulated epigenetic variation in the molecular etiology of schizophrenia, this study aimed to identify epigenetic variation associated with both a diagnosis of schizophrenia and elevated polygenic risk burden for the disease across multiple brain regions. Genome-wide DNA methylation was quantified in 262 post-mortem brain samples, representing tissue from four brain regions (prefrontal cortex, striatum, hippocampus and cerebellum) from 41 schizophrenia patients and 47 controls. We identified multiple disease-associated and polygenic risk score-associated differentially methylated positions and regions, many residing in the vicinity of genes previously implicated in schizophrenia including NCAM1, SYNPO, GBP4, PRDM9, GADD45B and DISC1. Our study represents the first analysis of epigenetic variation associated with schizophrenia across multiple brain regions and highlights the utility of polygenic risk scores for identifying molecular pathways associated with etiological variation in complex disease.
2016-11-10 | GSE89706 | GEO