scholarly journals Genetic overlap between educational attainment, schizophrenia and autism

2016 ◽  
Author(s):  
Varun Warrier ◽  
Richard AI Bethlehem ◽  
Daniel H Geschwind ◽  
Simon Baron-Cohen
Keyword(s):  
Educational Attainment ◽  
Cognitive Deficits ◽  
Enrichment Analysis ◽  
Biological Pathways ◽  
Risk Genes ◽  
Gene Sets ◽  
Key Points ◽  
Genetic Overlap ◽  
Psychiatric Conditions ◽  
Human Accelerated Regions

AbstractImportanceThe genetic relationship between cognition, autism, and schizophrenia is complex. It is unclear how genes that contribute to cognition also contribute to risk for autism and schizophrenia.ObjectiveTo investigate the interaction between genes related to cognition (measured via proxy through educational attainment, which we call ‘edu genes’) and genes/biological pathways that are atypical in autism and schizophrenia.DesignGenetic correlation and enrichment analysis were conducted to identify the interaction between edu genes and risk genes and biological pathways for autism or schizophrenia.ResultsFirst, edu genes are enriched in a specific developmental co-expression module that is also enriched for high confidence autism risk genes. Second, modules enriched for genes that are dysregulated in autism and schizophrenia are also enriched for edu genes. Finally, genes that overlap between the two above modules and educational attainment are significantly enriched for genes that flank human accelerated regions, suggesting increased positive selection for the overlapping gene sets.ConclusionOur results identify distinct co-expression modules where risk genes for the two psychiatric conditions interact with edu genes. This suggests specific pathways that contribute to both cognitive deficits and cognitive talents, in individuals with schizophrenia or autism.Key PointsQuestionHow do genes for educational attainment interact with risk genes for autism and schizophrenia?FindingsWe show that genes for educational attainment (edu genes) are significantly likely to be mutated in autism and intellectual disability. We further show that edu genes also interact with co-expression modules that are associated with autism or schizophrenia and are enriched for differentially expressed genes in autism or schizophrenia. Finally, we identify that the enrichment between risk genes for autism and schizophrenia and human accelerated regions are driven, in part, by their overlap with edu genes.MeaningEdu genes interact with schizophrenia and autism risk genes in specific pathways, contributing to both cognitive deficits and talents.

scholarly journals Dissecting Biological Pathways of Psychopathology using Cognitive Genomics

2021 ◽  
Author(s):  
Max Lam ◽  
Chia-Yen Chen ◽  
W. David Hill ◽  
Charley Xia ◽  
Ruoyu Tian ◽  
...  
Keyword(s):  
Early Childhood ◽  
Correlation Analysis ◽  
Cognitive Ability ◽  
Genetic Correlation ◽  
Cognitive Deficits ◽  
Cognitive Skills ◽  
Biological Pathways ◽  
General Cognitive Ability ◽  
Gene Sets ◽  
Cognitive Dimensions

Cognitive deficits are known to be related to most forms of psychopathology. Here, we perform local genetic correlation analysis as a means of identifying independent segments of the genome that show biologically interpretable pleiotropic associations between cognitive dimensions and psychopathology. We identified collective segments of the genome, which we call "meta-loci", that showed differential pleiotropic patterns for psychopathology relative to either General Cognitive Ability (GCA) or Non-Cognitive Skills (NCS). We observed that neurodevelopmental gene sets expressed during the prenatal-early childhood predominated in GCA-relevant meta-loci, while post-natal synaptic gene sets were more involved in NCS-relevant meta-loci. Notably, we found that GABA-ergic, cholinergic, and glutamatergic genes drove pleiotropic relationships within dissociable NCS meta-loci.

scholarly journals Systems Level Analysis and Identification of Pathways and Key Genes Associated with Delirium

Genes ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1225 ◽  
Author(s):  
Yukiko Takahashi ◽  
Tomoyoshi Terada ◽  
Yoshinori Muto
Keyword(s):  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
High Tendency ◽  
Diagnostic Biomarkers ◽  
Protein Protein Interaction ◽  
Gene Sets ◽  
Network Modules ◽  
Complex Detection ◽  
Human Accelerated Regions

Delirium is a complex pathophysiological process, and multiple contributing mechanisms have been identified. However, it is largely unclear how the genes associated with delirium contribute and which of them play key roles. In this study, the genes associated with delirium were retrieved from the Comparative Toxicogenomics Database (CTD) and integrated through a protein–protein interaction (PPI) network. Delirium-associated genes formed a highly interconnected PPI subnetwork, indicating a high tendency to interact and agglomerate. Using the Molecular Complex Detection (MCODE) algorithm, we identified the top two delirium-relevant network modules, M1 and M5, that have the most significant enrichments for the delirium-related gene sets. Functional enrichment analysis showed that genes related to neurotransmitter receptor activity were enriched in both modules. Moreover, analyses with genes located in human accelerated regions (HARs) provided evidence that HAR-Brain genes were overrepresented in the delirium-relevant network modules. We found that four of the HAR-Brain genes, namely APP, PLCB1, NPY, and HTR2A, in the M1 module were highly connected and appeared to exhibit hub properties, which might play vital roles in delirium development. Further understanding of the function of the identified modules and member genes could help to identify therapeutic intervention targets and diagnostic biomarkers for delirium.

DECO: a framework for jointly analyzing de novo and rare case/control variants, and biological pathways

2021 ◽  
Author(s):  
Tan-Hoang Nguyen ◽  
Xin He ◽  
Ruth C Brown ◽  
Bradley T Webb ◽  
Kenneth S Kendler ◽  
...  
Keyword(s):  
Rare Variant ◽  
Rare Case ◽  
Rare Variants ◽  
De Novo ◽  
Case Control ◽  
Biological Pathways ◽  
Integrated Analysis ◽  
Risk Genes ◽  
Additional Risk ◽  
Gene Sets

Abstract Motivation: Rare variant-based analyses are beginning to identify risk genes for neuropsychiatric disorders and other diseases. However, the identified genes only account for a fraction of predicted causal genes. Recent studies have shown that rare damaging variants are significantly enriched in specific gene-sets. Methods which are able to jointly model rare variants and gene-sets to identify enriched gene-sets and use these enriched gene-sets to prioritize additional risk genes could improve understanding of the genetic architecture of diseases. Results: We propose DECO (Integrated analysis of de novo mutations, rare case/control variants and omics information via gene-sets), an integrated method for rare-variant and gene-set analysis. The method can (i) test the enrichment of gene-sets directly within the statistical model, and (ii) use enriched gene-sets to rank existing genes and prioritize additional risk genes for tested disorders. In simulations, DECO performs better than a homologous method that uses only variant data. To demonstrate the application of the proposed protocol, we have applied this approach to rare-variant datasets of schizophrenia. Compared with a method which only uses variant information, DECO is able to prioritize additional risk genes. Availability: DECO can be used to analyze rare-variants and biological pathways or cell types for any disease. The package is available on Github https://github.com/hoangtn/DECO.

scholarly journals Integrative proteomics and pharmacogenomics analysis of methylphenidate treatment response

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Bruna S. da Silva ◽  
Douglas T. Leffa ◽  
Walter O. Beys-da-Silva ◽  
Iraci L. S. Torres ◽  
Diego L. Rovaris ◽  
...  
Keyword(s):  
Synaptic Transmission ◽  
Treatment Response ◽  
Clinical Sample ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Biological Pathways ◽  
Functional Enrichment ◽  
Cellular Respiration ◽  
Biological Processes ◽  
Gene Sets

AbstractTranscriptomics and candidate gene/protein expression studies have indicated several biological processes modulated by methylphenidate (MPH), widely used in attention-deficit/hyperactivity disorder (ADHD) treatment. However, the lack of a differential proteomic profiling of MPH treatment limits the understanding of the most relevant mechanisms by which MPH exerts its pharmacological effects at the molecular level. Therefore, our aim is to investigate the MPH-induced proteomic alterations using an experimental design integrated with a pharmacogenomic analysis in a translational perspective. Proteomic analysis was performed using the cortices of Wistar-Kyoto rats, which were treated by gavage with MPH (2 mg/kg) or saline for two weeks (n = 6/group). After functional enrichment analysis of the differentially expressed proteins (DEP) in rats, the significant biological pathways were tested for association with MPH response in adults with ADHD (n = 189) using genome-wide data. Following MPH treatment in rats, 98 DEPs were found (P < 0.05 and FC < −1.0 or > 1.0). The functional enrichment analysis of the DEPs revealed 18 significant biological pathways (gene-sets) modulated by MPH, including some with recognized biological plausibility, such as those related to synaptic transmission. The pharmacogenomic analysis in the clinical sample evaluating these pathways revealed nominal associations for gene-sets related to neurotransmitter release and GABA transmission. Our results, which integrate proteomics and pharmacogenomics, revealed putative molecular effects of MPH on several biological processes, including oxidative stress, cellular respiration, and metabolism, and extended the results involving synaptic transmission pathways to a clinical sample. These findings shed light on the molecular signatures of MPH effects and possible biological sources of treatment response variability.

scholarly journals NoRCE: non-coding RNA sets cis enrichment tool

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gulden Olgun ◽  
Afshan Nabi ◽  
Oznur Tastan
Keyword(s):  
Expression Patterns ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
Fruit Fly ◽  
Relevant Information ◽  
R Package ◽  
Data Repository ◽  
Biologically Relevant ◽  
Gene Sets ◽  
Data Files

Abstract Background While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint at a functional association. Results We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. Conclusions NoRCE is a platform-independent, user-friendly, comprehensive R package that can be used to gain insight into the functional importance of a list of ncRNAs of any type. The tool offers flexibility to conduct the users’ preferred set of analyses by designing their own pipeline of analysis. NoRCE is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

scholarly journals Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Scrub Typhus ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Acid Base ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.

scholarly journals Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mike Fang ◽  
Brian Richardson ◽  
Cheryl M. Cameron ◽  
Jean-Eudes Dazard ◽  
Mark J. Cameron
Keyword(s):  
Drug Targets ◽  
T Regulatory Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Regulatory Cells ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Gastroenteropancreatic Neuroendocrine Tumor ◽  
Public Datasets

Abstract Background In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets. Results We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting. Conclusions dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.

Robust Metabolic Transcriptional Components in 34,494 Patient-Derived Samples and Cell Lines

2021 ◽  
Author(s):  
Vincent Christiaan Leeuwenburgh ◽  
Carlos G. Urzúa-Traslaviña ◽  
Arkajyoti Bhattacharya ◽  
Marthe T.C. Walvoort ◽  
Mathilde Jalving ◽  
...  
Keyword(s):  
Cell Lines ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Metabolic Processes ◽  
Gene Sets ◽  
Transcriptional Changes ◽  
Cancer Tissues ◽  
Tumor Biopsies ◽  
Transcriptional Landscape

Abstract Background: Patient-derived bulk expression profiles of cancers can provide insight into transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Hence, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. However, consensus Independent Component Analyses (c-ICA) can capture statistically independent transcriptional footprints, of both subtle and more pronounced metabolic processes. Methods: We performed c-ICA with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. Gene set enrichment analysis with 608 gene sets that describe metabolic processes was performed to identify transcriptional components enriched for metabolic processes (mTCs). The activity of these mTCs were determined in all samples to create a metabolic transcriptional landscape. Results: A set of 555 mTCs were identified of which many were robust across different datasets, platforms, and patient-derived tissues and cell lines. We demonstrate how the metabolic transcriptional landscape defined by the activity of these mTCs in samples can be used to explore associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. Conclusions: To facilitate the use of our transcriptional metabolic landscape, we have provided access to all data via a web portal ( www.themetaboliclandscapeofcancer.com ). We believe this resource will contribute to the formulation of new hypotheses on how to metabolically engage the tumor or its (immune) microenvironment.

scholarly journals TFEA.ChIP: a tool kit for transcription factor binding site enrichment analysis capitalizing on ChIP-seq datasets

2019 ◽  
Vol 35 (24) ◽  
pp. 5339-5340 ◽  
Author(s):  
Laura Puente-Santamaria ◽  
Wyeth W Wasserman ◽  
Luis del Peso
Keyword(s):  
Genomic Analysis ◽  
Enrichment Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Web Based ◽  
Factor Binding Site ◽  
Gene Sets ◽  
Transcription Regulators ◽  
Computational Identification ◽  
On Chip

Abstract Summary The computational identification of the transcription factors (TFs) [more generally, transcription regulators, (TR)] responsible for the co-regulation of a specific set of genes is a common problem found in genomic analysis. Herein, we describe TFEA.ChIP, a tool that makes use of ChIP-seq datasets to estimate and visualize TR enrichment in gene lists representing transcriptional profiles. We validated TFEA.ChIP using a wide variety of gene sets representing signatures of genetic and chemical perturbations as input and found that the relevant TR was correctly identified in 126 of a total of 174 analyzed. Comparison with other TR enrichment tools demonstrates that TFEA.ChIP is an highly customizable package with an outstanding performance. Availability and implementation TFEA.ChIP is implemented as an R package available at Bioconductor https://www.bioconductor.org/packages/devel/bioc/html/TFEA.ChIP.html and github https://github.com/LauraPS1/TFEA.ChIP_downloads. A web-based GUI to the package is also available at https://www.iib.uam.es/TFEA.ChIP/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Identification of biological pathways and genes associated with neurogenic heterotopic ossification by text mining

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8276 ◽  
Author(s):  
Yichong Zhang ◽  
Yuanbo Zhan ◽  
Yuhui Kou ◽  
Xiaofeng Yin ◽  
Yanhua Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Text Mining ◽  
Heterotopic Ossification ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Egf Receptor ◽  
Enrichment Analysis ◽  
Biological Pathways ◽  
Specific Gene ◽  
Cord Injury

Background Neurogenic heterotopic ossification is a disorder of aberrant bone formation affecting one in five patients sustaining a spinal cord injury or traumatic brain injury (SCI-TBI-HO). However, the underlying mechanisms of SCI-TBI-HO have proven difficult to elucidate. The aim of the present study is to identify the most promising candidate genes and biological pathways for SCI-TBI-HO. Methods In this study, we used text mining to generate potential explanations for SCI-TBI-HO. Moreover, we employed several additional datasets, including gene expression profile data, drug data and tissue-specific gene expression data, to explore promising genes that associated with SCI-TBI-HO. Results We identified four SCI-TBI-HO-associated genes, including GDF15, LDLR, CCL2, and CLU. Finally, using enrichment analysis, we identified several pathways, including integrin signaling, insulin pathway, internalization of ErbB1, urokinase-type plasminogen activator and uPAR-mediated signaling, PDGFR-beta signaling pathway, EGF receptor (ErbB1) signaling pathway, and class I PI3K signaling events, which may be associated with SCI-TBI-HO. Conclusions These results enhance our understanding of the molecular mechanisms of SCI-TBI-HO and offer new leads for researchers and innovative therapeutic strategies.

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