Genome Wide Gene Expression Studies in Mood Disorders

2006 ◽  
Vol 10 (4) ◽  
pp. 444-454 ◽  
Author(s):  
Adolfo Sequeira ◽  
Gustavo Turecki
Keyword(s):  
Gene Expression ◽  
Mood Disorders ◽  
Expression Studies ◽  
Genome Wide ◽  
Gene Expression Studies ◽  
Genome Wide Gene Expression

scholarly journals Threshold-free high-power methods for the ontological analysis of genome-wide gene-expression studies

2007 ◽  
Vol 8 (5) ◽  
pp. R74 ◽  
Author(s):  
Björn Nilsson ◽  
Petra Håkansson ◽  
Mikael Johansson ◽  
Sven Nelander ◽  
Thoas Fioretos
Keyword(s):  
Gene Expression ◽  
High Power ◽  
Expression Studies ◽  
Genome Wide ◽  
Ontological Analysis ◽  
Gene Expression Studies ◽  
Genome Wide Gene Expression

scholarly journals Systematic review of genome-wide gene expression studies of bipolar disorder

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Fayaz Seifuddin ◽  
Mehdi Pirooznia ◽  
Jennifer T Judy ◽  
Fernando S Goes ◽  
James B Potash ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systematic Review ◽  
Bipolar Disorder ◽  
Expression Studies ◽  
Genome Wide ◽  
Gene Expression Studies ◽  
Genome Wide Gene Expression

scholarly journals variancePartition: Interpreting drivers of variation in complex gene expression studies

2016 ◽  
Author(s):  
Gabriel E Hoffman ◽  
Eric E Schadt
Keyword(s):  
Gene Expression ◽  
Multiple Sources ◽  
Disease Biology ◽  
Expression Studies ◽  
Genome Wide ◽  
A Genome ◽  
Gene Expression Studies

As genomics studies become more complex and consider multiple sources of biological and technical variation, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation with a genome-wide summary, and identify genes that deviate from the genome-wide trend. variancePartition enables rapid interpretation of complex gene expression studies and is applicable to many genomics assays.

scholarly journals JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts

2017 ◽  
Author(s):  
Chris Chatzinakos ◽  
Donghyung Lee ◽  
Bradley T Webb ◽  
Vladimir I Vladimirov ◽  
Kenneth S Kendler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Supplementary Information ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Expression Studies ◽  
Genome Wide ◽  
Gene Level ◽  
Supplementary Material ◽  
Gene Expression Studies

AbstractMotivationTo increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on i) summary statistics from genome-wide association studies (GWAS) and ii) linkage disequilibrium (LD) patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals.ResultsWe propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses i) cis-eQTL SNPs from the latest expression studies and ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and ii), to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of [email protected] informationSupplementary material is available at Bioinformatics online.

scholarly journals RNAlater and flash freezing storage methods nonrandomly influence observed gene expression in RNAseq experiments

2018 ◽  
Author(s):  
Courtney N. Passow ◽  
Thomas J. Y. Kono ◽  
Bethany A. Stahl ◽  
James B. Jaggard ◽  
Alex C. Keene ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liquid Nitrogen ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gc Content ◽  
Gene Expression Profiles ◽  
Expression Studies ◽  
Genome Wide ◽  
Differential Gene ◽  
Genome Wide Gene Expression

AbstractRNA-sequencing is a popular next-generation sequencing technique for assaying genome-wide gene expression profiles. Nonetheless, it is susceptible to biases that are introduced by sample handling prior gene expression measurements. Two of the most common methods for preserving samples in both field-based and laboratory conditions are submersion in RNAlater and flash freezing in liquid nitrogen. Flash freezing in liquid nitrogen can be impractical, particularly for field collections. RNAlater is a solution for stabilizing tissue for longer-term storage as it rapidly permeates tissue to protect cellular RNA. In this study, we assessed genome-wide expression patterns in 30 day old fry collected from the same brood at the same time point that were flash-frozen in liquid nitrogen and stored at −80°C or submerged and stored in RNAlater at room temperature, simulating conditions of fieldwork. We show that sample storage is a significant factor influencing observed differential gene expression. In particular, genes with elevated GC content exhibit higher observed expression levels in liquid nitrogen flash-freezing relative to RNAlater-storage. Further, genes with higher expression in RNAlater relative to liquid nitrogen experience disproportionate enrichment for functional categories, many of which are involved in RNA processing. This suggests that RNAlater may elicit a physiological response that has the potential to bias biological interpretations of expression studies. The biases introduced to observed gene expression arising from mimicking many field-based studies are substantial and should not be ignored.

scholarly journals A measure of agreement across numerous conditions: Reproducibility of co-expression networks across tissues

2017 ◽  
Author(s):  
Alejandro Cáceres ◽  
Juan R. González
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Expression Data ◽  
Full Agreement ◽  
Statistical Measures ◽  
Expression Studies ◽  
Genome Wide ◽  
Gene Expression Studies ◽  
Microarray Studies ◽  
Level Of Agreement

AbstractThere is great interest to study how co-expression gene networks change across tissues. However, the reproducibility assessment of these studies is challenged by a lack of fully confirmatory experiments from independent researchers. While an increment in the number of studies with expression data for several tissues is expected, statistical measures are still needed to assess the reproducibility between studies. We identified a gap in the statistical literature concerning the assessment of agreement between studies across numerous conditions. The gap precluded us to test, using standard statistics, the level of agreement between the GTEX (RNAseq) and BRAINEAC (microarray) studies to distinguish the structure of co-expression networks across four brain tissues. We propose a generalization of a classical measure of agreement, Cohen’s κ, derive its distributional characteristics and determine its reliability properties. In the gene expression studies, our generalization of κ showed full agreement for genome-wide networks in BRAINEAC benchmarked against GTEX, and highest agreement for brain specific pathways. Our highly interpretable measure can contribute to anticipated efforts on reproducibility research.

scholarly journals Comprehensive Integration of Genome-Wide Association and Gene Expression Studies Reveals Novel Gene Signatures and Potential Therapeutic Targets for Helicobacter pylori-Induced Gastric Disease

2021 ◽  
Vol 12 ◽  
Author(s):  
Mohamed Tarek Badr ◽  
Mohamed Omar ◽  
Georg Häcker
Keyword(s):  
Gene Expression ◽  
Helicobacter Pylori ◽  
Cell Line ◽  
Gene Signature ◽  
Genome Wide Association ◽  
Expression Studies ◽  
Genome Wide ◽  
Human Pathology ◽  
Gene Expression Studies ◽  
H Pylori

Helicobacter pylori is a gram-negative bacterium that colonizes the human gastric mucosa and can lead to gastric inflammation, ulcers, and stomach cancer. Due to the increase in H. pylori antimicrobial resistance new methods to identify the molecular mechanisms of H. pylori-induced pathology are urgently needed. Here we utilized a computational biology approach, harnessing genome-wide association and gene expression studies to identify genes and pathways determining disease development. We mined gene expression data related to H. pylori-infection and its complications from publicly available databases to identify four human datasets as discovery datasets and used two different multi-cohort analysis pipelines to define a H. pylori-induced gene signature. An initial Helicobacter-signature was curated using the MetaIntegrator pipeline and validated in cell line model datasets. With this approach we identified cell line models that best match gene regulation in human pathology. A second analysis pipeline through NetworkAnalyst was used to refine our initial signature. This approach defined a 55-gene signature that is stably deregulated in disease conditions. The 55-gene signature was validated in datasets from human gastric adenocarcinomas and could separate tumor from normal tissue. As only a small number of H. pylori patients develop cancer, this gene-signature must interact with other host and environmental factors to initiate tumorigenesis. We tested for possible interactions between our curated gene signature and host genomic background mutations and polymorphisms by integrating genome-wide association studies (GWAS) and known oncogenes. We analyzed public databases to identify genes harboring single nucleotide polymorphisms (SNPs) associated with gastric pathologies and driver genes in gastric cancers. Using this approach, we identified 37 genes from GWA studies and 61 oncogenes, which were used with our 55-gene signature to map gene-gene interaction networks. In conclusion, our analysis defines a unique gene signature driven by H. pylori-infection at early phases and that remains relevant through different stages of pathology up to gastric cancer, a stage where H. pylori itself is rarely detectable. Furthermore, this signature elucidates many factors of host gene and pathway regulation in infection and can be used as a target for drug repurposing and testing of infection models suitability to investigate human infection.

scholarly journals Coding and Noncoding Gene Expression Biomarkers in Mood Disorders and Schizophrenia

2013 ◽  
Vol 35 ◽  
pp. 11-21 ◽  
Author(s):  
Firoza Mamdani ◽  
Maureen V. Martin ◽  
Todd Lencz ◽  
Brandi Rollins ◽  
Delbert G. Robinson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Treatment Response ◽  
Mood Disorders ◽  
First Episode ◽  
Molecular Fingerprint ◽  
Complex Disorders ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
Consequences Of Disease

Mood disorders and schizophrenia are common and complex disorders with consistent evidence of genetic and environmental influences on predisposition. It is generally believed that the consequences of disease, gene expression, and allelic heterogeneity may be partly the explanation for the variability observed in treatment response. Correspondingly, while effective treatments are available for some patients, approximately half of the patients fail to respond to current neuropsychiatric treatments. A number of peripheral gene expression studies have been conducted to understand these brain-based disorders and mechanisms of treatment response with the aim of identifying suitable biomarkers and perhaps subgroups of patients based upon molecular fingerprint. In this review, we summarize the results from blood-derived gene expression studies implemented with the aim of discovering biomarkers for treatment response and classification of disorders. We include data from a biomarker study conducted in first-episode subjects with schizophrenia, where the results provide insight into possible individual biological differences that predict antipsychotic response. It is concluded that, while peripheral studies of expression are generating valuable results in pathways involving immune regulation and response, larger studies are required which hopefully will lead to robust biomarkers for treatment response and perhaps underlying variations relevant to these complex disorders.

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