scholarly journals Deconvolution of Transcriptional Networks Identifies TCF4 as a Master Regulator in Schizophrenia

2017 ◽  
Author(s):  
Abolfazl Doostparast Torshizi ◽  
Chris Armoskus ◽  
Hanwen Zhang ◽  
Marc P. Forrest ◽  
Siwei Zhang ◽  
...  
Keyword(s):  
Gene Network ◽  
Cultured Cells ◽  
Association Studies ◽  
Large Body ◽  
Transcriptional Network ◽  
Transcriptional Networks ◽  
Genome Wide Association Studies ◽  
Rna Seq ◽  
Genome Wide ◽  
Neuropsychiatric Diseases

AbstractTissue-specific reverse engineering of transcriptional networks has uncovered master regulators (MRs) of cellular networks in various cancers, yet the application of this method to neuropsychiatric disorders is largely unexplored. Here, using RNA-Seq data on postmortem dorsolateral prefrontal cortex (DLPFC) from schizophrenia (SCZ) patients and control subjects, we deconvolved the transcriptional network to identify MRs that mediate expression of a large body of target genes. Together with an independent RNA-Seq data on cultured cells derived from olfactory neuroepithelium, we identified TCF4, a leading SCZ risk locus implicated by genome-wide association studies, as one of the top candidate MRs that may be potentially dysregulated in SCZ. We validated the dysregulated TCF4-related transcriptional network through examining the transcription factor binding footprints inferred from human induced pluripotent stem cell (hiPSC)-derived neuronal ATAC-Seq data, as well as direct binding sites obtained from ChIP-seq data in SH-SY5Y cells. The predicted TCF4 transcriptional targets were enriched for genes showing transcriptomic changes upon knockdown of TCF4 in hiPSC-derived neural progenitor cells (NPC) and glutamatergic neurons (Glut_N), based on observations from three separate cell lines. The altered TCF4 gene network perturbations in NPC, as compared to that in Glut_N, was more similar to the expression differences in the TCF4 gene network observed in the DLPFC of individuals with SCZ. Moreover, TCF4-associated gene expression changes in NPC were more enriched than Glut_N for pathways involved in neuronal activity, genome-wide significant SCZ risk genes, and SCZ-associated de novo mutations. Our results suggest that TCF4 may potentially serve as a MR of a gene network that confers susceptibility to SCZ at early stage of neurodevelopment, highlighting the importance of network dysregulation involving core genes and many hundreds of peripheral genes in conferring susceptibility to neuropsychiatric diseases.

scholarly journals Informing disease modelling with brain-relevant functional genomic annotations

Brain ◽  
2019 ◽  
Vol 142 (12) ◽  
pp. 3694-3712 ◽  
Author(s):  
Regina H Reynolds ◽  
John Hardy ◽  
Mina Ryten ◽  
Sarah A Gagliano Taliun
Keyword(s):  
Genetic Association ◽  
Association Studies ◽  
Therapeutic Targets ◽  
Genome Wide Association ◽  
Disease Modelling ◽  
Genome Wide Association Studies ◽  
Functional Genomic ◽  
Genome Wide ◽  
Neuropsychiatric Diseases ◽  
Association Data

How can we best translate the success of genome-wide association studies for neurological and neuropsychiatric diseases into therapeutic targets? Reynolds et al. critically assess existing brain-relevant functional genomic annotations and the tools available for integrating such annotations with summary-level genetic association data.

scholarly journals Transcriptome Analysis in Domesticated Species: Challenges and Strategies

2015 ◽  
Vol 9S4 ◽  
pp. BBI.S29334 ◽  
Author(s):  
Jessica P. Hekman ◽  
Jennifer L Johnson ◽  
Anna V. Kukekova
Keyword(s):  
Complex Traits ◽  
Gene Networks ◽  
Association Studies ◽  
Cultural Value ◽  
Genomic Research ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Rna Seq ◽  
Genome Wide ◽  
Genome Assemblies

Domesticated species occupy a special place in the human world due to their economic and cultural value. In the era of genomic research, domesticated species provide unique advantages for investigation of diseases and complex phenotypes. RNA sequencing, or RNA-seq, has recently emerged as a new approach for studying transcriptional activity of the whole genome, changing the focus from individual genes to gene networks. RNA-seq analysis in domesticated species may complement genome-wide association studies of complex traits with economic importance or direct relevance to biomedical research. However, RNA-seq studies are more challenging in domesticated species than in model organisms. These challenges are at least in part associated with the lack of quality genome assemblies for some domesticated species and the absence of genome assemblies for others. In this review, we discuss strategies for analyzing RNA-seq data, focusing particularly on questions and examples relevant to domesticated species.

scholarly journals Multivariate genome-wide association studies on the tissue compartments of human brain identify novel loci underpinning brain development and neuropsychiatric outcomes.

2021 ◽  
Author(s):  
Chun Chieh Fan ◽  
Robert Loughnan ◽  
Diliana Pechva ◽  
Chi-Hua Chen ◽  
Donald Hagler ◽  
...  
Keyword(s):  
Human Brain ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Risk Tolerance ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Imaging Features ◽  
Genome Wide ◽  
Neuropsychiatric Diseases ◽  
Tissue Compartments

It is important to understand the molecular determinants for microstructures of human brain. However, past genome-wide association studies (GWAS) on microstructures of human brain have had limited results due to methodological constraints. Here, we adopt advanced imaging processing methods and multivariate GWAS on two large scale imaging genetic datasets (UK Biobank and Adolescent Brain Cognitive Development study) to identify and validate key genetic association signals. We discovered 503 unique genetic loci that explained more than 50% of the average heritability across imaging features sensitive to tissue compartments. The genome-wide signals are strongly overlapped with neuropsychiatric diseases, cognitive functions, risk tolerance, and immune responses. Our results implicate the shared molecular mechanisms between tissue microstructures of brain and neuropsychiatric outcomes with astrocyte involvement in the early developmental stage.

scholarly journals Making sense of the cause of Crohn’s – a new look at an old disease

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2510 ◽  
Author(s):  
Anthony W. Segal
Keyword(s):  
Association Studies ◽  
Large Body ◽  
Adaptive Immune Response ◽  
Granulomatous Inflammation ◽  
Enteric Infection ◽  
Genome Wide Association Studies ◽  
Adaptive Immune ◽  
Making Sense ◽  
Technological Advances ◽  
Genome Wide

The cause of Crohn’s disease (CD) has posed a conundrum for at least a century. A large body of work coupled with recent technological advances in genome research have at last started to provide some of the answers. Initially this review seeks to explain and to differentiate between bowel inflammation in the primary immunodeficiencies that generally lead to very early onset diffuse bowel inflammation in humans and in animal models, and the real syndrome of CD. In the latter, a trigger, almost certainly enteric infection by one of a multitude of organisms, allows the faeces access to the tissues, at which stage the response of individuals predisposed to CD is abnormal. Direct investigation of patients’ inflammatory response together with genome-wide association studies (GWAS) and DNA sequencing indicate that in CD the failure of acute inflammation and the clearance of bacteria from the tissues, and from within cells, is defective. The retained faecal products result in the characteristic chronic granulomatous inflammation and adaptive immune response. In this review I will examine the contemporary evidence that has led to this understanding, and look for explanations for the recent dramatic increase in the incidence of this disease.

scholarly journals Chromatin-based techniques map DNA interaction landscapes in psoriasis susceptibility loci and highlight KLF4 as a target gene in 9q31

2019 ◽  
Author(s):  
Helen Ray-Jones ◽  
Kate Duffus ◽  
Amanda McGovern ◽  
Paul Martin ◽  
Chenfu Shi ◽  
...  
Keyword(s):  
Target Gene ◽  
Target Genes ◽  
Association Studies ◽  
Dna Interaction ◽  
Genome Wide Association Studies ◽  
Susceptibility Loci ◽  
Rna Seq ◽  
Causal Gene ◽  
Genome Wide ◽  
Crispr Activation

AbstractGenome-wide association studies (GWAS) have uncovered many genetic risk loci for psoriasis, yet many remain uncharacterised in terms of the causal gene and their biological mechanism in disease. Here, we use a disease-focused Capture Hi-C experiment to link psoriasis-associated variants with their target genes in psoriasis-relevant cell lines (HaCaT keratinocytes and My-La CD8+ T cells). We confirm previously assigned genes, suggest novel candidates and provide evidence for complexity at psoriasis GWAS loci. In the 9q31 risk locus we combine further epigenomic evidence to demonstrate how the psoriasis association forms a functional interaction with the distant (>500 kb) KLF4 gene. We use CRISPR activation coupled with RNA-seq to demonstrate how activation of psoriasis-associated enhancers upregulates KLF4 in HaCaT cells. Our study design provides a robust pipeline for following up on GWAS disease-associated variants, paving the way for functional translation of genetic findings into clinical benefit.

scholarly journals The paltry power of priors versus populations

2019 ◽  
Author(s):  
Jianan Zhan ◽  
Dan E. Arking ◽  
Joel S. Bader
Keyword(s):  
Population Size ◽  
Multiple Testing ◽  
Association Studies ◽  
Genome Wide Association ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Significant Finding ◽  
Rna Seq ◽  
Test Power ◽  
Genome Wide

AbstractBiological experiments often involve hypothesis testing at the scale of thousands to millions of tests. Alleviating the multiple testing burden has been a goal of many methods designed to boost test power by focusing tests on the alternative hypotheses most likely to be true. Very often, these methods either explicitly or implicitly make use of prior probabilities that bias significance for favored sets thought to be enriched for significant finding. Nevertheless, most genomics experiments, and in particular genome-wide association studies (GWAS), still use traditional univariate tests rather than more sophisticated approaches. Here we use GWAS to demonstrate why unbiased tests remain in favor. We calculate test power assuming perfect knowledge of a prior distribution and then derive the population size increase required to provided the same boost without a prior. We show that population size is exponentially more important than prior, providing a rigorous explanation for the observed avoidance of prior-based methods.Author summaryBiological experiments often test thousands to millions of hypotheses. Gene-based tests for human RNA-Seq data, for example, involve approximately 20,000; genome-wide association studies (GWAS) involve about 1 million effective tests. The conventional approach is to perform individual tests and then apply a Bonferroni correction to account for multiple testing. This approach implies a single-test p-value of 2.5 × 10−6 for RNA-Seq experiments, and a p-value of 5 × 10−8 for GWAS, to control the false-positive rate at a conventional value of 0.05. Many methods have been proposed to alleviate the multiple-testing burden by incorporating a prior probability that boosts the significance for a subset of candidate genes or variants. At the extreme limit, only the candidate set is tested, corresponding to a decreased multiple testing burden. Despite decades of methods development, prior-based tests have not been generally used. Here we compare the power increase possible with a prior with the increase possible with a much simpler strategy of increasing a study size. We show that increasing the population size is exponentially more valuable than increasing the strength of prior, even when the true prior is known exactly. These results provide a rigorous explanation for the continued use of simple, robust methods rather than more sophisticated approaches.

scholarly journals Essential genes from genome-wide screenings as a resource for neuropsychiatric disorders gene discovery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wei Zhang ◽  
Joao Quevedo ◽  
Gabriel R. Fries
Keyword(s):  
Human Brain ◽  
Cancer Cell ◽  
Association Studies ◽  
Neuropsychiatric Disorders ◽  
Essential Genes ◽  
De Novo Mutation ◽  
Genome Wide Association Studies ◽  
Transcriptomic Data ◽  
Genome Wide ◽  
Neuropsychiatric Diseases

AbstractGenome-wide screenings of “essential genes”, i.e., genes required for an organism or cell survival, have been traditionally conducted in vitro in cancer cell lines, limiting the translation of results to other tissues and non-cancerous cells. Recently, an in vivo screening was conducted in adult mouse striatum tissue, providing the first genome-wide dataset of essential genes in neuronal cells. Here, we aim to investigate the role of essential genes in brain development and disease risk with a comprehensive set of bioinformatics tools, including integration with transcriptomic data from developing human brain, publicly available data from genome-wide association studies, de novo mutation datasets for different neuropsychiatric disorders, and case–control transcriptomic data from postmortem brain tissues. For the first time, we found that the expression of neuronal essential genes (NEGs) increases before birth during the early development of human brain and maintains a relatively high expression after birth. On the contrary, common essential genes from cancer cell line screenings (ACEGs) tend to be expressed at high levels during development but quickly drop after birth. Both gene sets were enriched in neurodevelopmental disorders, but only NEGs were robustly associated with neuropsychiatric disorders risk genes. Finally, NEGs were more likely to show differential expression in the brains of neuropsychiatric disorders patients than ACEGs. Overall, genome-wide central nervous system screening of essential genes can provide new insights into neuropsychiatric diseases.

scholarly journals Learning Gene Networks Underlying Clinical Phenotypes Using SNP Perturbations

2018 ◽  
Author(s):  
Calvin McCarter ◽  
Judie Howrylak ◽  
Seyoung Kim
Keyword(s):  
Gene Networks ◽  
Gene Network ◽  
Molecular Mechanisms ◽  
Graphical Model ◽  
Sequence Data ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Clinical Phenotypes ◽  
Genome Wide

AbstractRecent technologies are generating an abundance of genome sequence data and molecular and clinical phenotype data, providing an opportunity to understand the genetic architecture and molecular mechanisms underlying diseases. Previous approaches have largely focused on the co-localization of single-nucleotide polymorphisms (SNPs) associated with clinical and expression traits, each identified from genome-wide association studies and expression quantitative trait locus (eQTL) mapping, and thus have provided only limited capabilities for uncovering the molecular mechanisms behind the SNPs influencing clinical phenotypes. Here we aim to extract rich information on the functional role of trait-perturbing SNPs that goes far beyond this simple co-localization. We introduce a computational framework called Perturb-Net for learning the gene network that modulates the influence of SNPs on phenotypes, using SNPs as naturally occurring perturbation of a biological system. Perturb-Net uses a probabilistic graphical model to directly model both the cascade of perturbation from SNPs to the gene network to the phenotype network and the network at each layer of molecular and clinical phenotypes. Perturb-Net learns the entire model by solving a single optimization problem with an extremely fast algorithm that can analyze human genome-wide data within a few hours. In our analysis of asthma data, for a locus that was previously implicated in asthma susceptibility but for which little is known about the molecular mechanism underlying the association, Perturb-Net revealed the gene network modules that mediate the influence of the SNP on asthma phenotypes. Many genes in this network module were well supported in the literature as asthma-related.

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