scholarly journals Meta-analysis and profiling of cardiac expression modules

2008 ◽  
Vol 35 (3) ◽  
pp. 305-315 ◽  
Author(s):  
Uri David Akavia ◽  
Dafna Benayahu
Keyword(s):  
Heart Failure ◽  
Large Scale ◽  
Meta Analysis ◽  
Gene Expression Omnibus ◽  
Biological Knowledge ◽  
Map Algorithm ◽  
Large Scale Analysis ◽  
Expression Of Genes ◽  
Prior Biological Knowledge ◽  
Clinical Conditions

Heart failure is a complex, complicated disease that is not yet fully understood. We used the Module Map algorithm to uncover groups of genes that have a similar pattern of expression under various conditions of heart stress. These groups of genes are called modules and may serve as computational predictions of biological pathways for the various clinical situations. The Module Map algorithm allows a large-scale analysis of genes expressed. We applied this algorithm to 700 different mouse experiments downloaded from the Gene Expression Omnibus database, which identified 884 modules. The analysis reconstructed partially known principles that play a role in governing the response of heart to stress, thus demonstrating the strength of the method. We have shown a role of genes related to the immune system in conditions of heart remodeling and failure. We have also shown changes in the expression of genes involved with energy metabolism and changes in the expression of contractile proteins of the heart following myocardial infarction. When focusing on another module we noted a new correlation between genes related to osteogenesis and heart failure, including Runx2 and Ahsg, whose role in heart failure was unknown so far. Despite a lack of prior biological knowledge, the Module Map algorithm has reconstructed known pathways, which demonstrates the strength of this new method for analyzing gene profiles related to clinical phenomenon. The method and the analysis presented are a new avenue to uncover the correlation of clinical conditions to the molecular level.

scholarly journals Using a Two-Sample Mendelian Randomization Method in Assessing the Causal Relationships Between Human Blood Metabolites and Heart Failure

2021 ◽  
Vol 8 ◽  
Author(s):  
Zixian Wang ◽  
Shiyu Chen ◽  
Qian Zhu ◽  
Yonglin Wu ◽  
Guifeng Xu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Human Blood ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
Blood Metabolites ◽  
Causal Relationships

Background: Heart failure (HF) is the main cause of morbidity and mortality worldwide, and metabolic dysfunction is an important factor related to HF pathogenesis and development. However, the causal effect of blood metabolites on HF remains unclear.Objectives: Our chief aim is to investigate the causal relationships between human blood metabolites and HF risk.Methods: We used an unbiased two-sample Mendelian randomization (MR) approach to assess the causal relationships between 486 human blood metabolites and HF risk. Exposure information was obtained from Sample 1, which is the largest metabolome-based genome-wide association study (mGWAS) data containing 7,824 Europeans. Outcome information was obtained from Sample 2, which is based on the results of a large-scale GWAS meta-analysis of HF and contains 47,309 cases and 930,014 controls of Europeans. The inverse variance weighted (IVW) model was used as the primary two-sample MR analysis method and followed the sensitivity analyses, including heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis.Results: We observed that 11 known metabolites were potentially related to the risk of HF after using the IVW method (P < 0.05). After adding another four MR models and performing sensitivity analyses, we found a 1-SD increase in the xenobiotics 4-vinylphenol sulfate was associated with ~22% higher risk of HF (OR [95%CI], 1.22 [1.07–1.38]).Conclusions: We revealed that the 4-vinylphenol sulfate may nominally increase the risk of HF by 22% after using a two-sample MR approach. Our findings may provide novel insights into the pathogenesis underlying HF and novel strategies for HF prevention.

scholarly journals Cortical Thickness Trajectories across the Lifespan: Data from 17,075 healthy individuals aged 3-90 years

2020 ◽  
Author(s):  
Sophia Frangou ◽  
Amirhossein Modabbernia ◽  
Gaelle E Doucet ◽  
Efstathios Papachristou ◽  
Steven CR Williams ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Large Scale ◽  
Meta Analysis ◽  
General Pattern ◽  
Individual Variability ◽  
Anterior Cingulate ◽  
Healthy Individuals ◽  
Large Scale Analysis ◽  
Age Related ◽  
Steep Decrease

AbstractDelineating age-related cortical trajectories in healthy individuals is critical given the association of cortical thickness with cognition and behaviour. Previous research has shown that deriving robust estimates of age-related brain morphometric changes requires large-scale studies. In response, we conducted a large-scale analysis of cortical thickness in 17,075 individuals aged 3-90 years by pooling data through the Lifespan Working group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium. We used fractional polynomial (FP) regression to characterize age-related trajectories in cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma (LMS) method. Inter-individual variability was estimated using meta-analysis and one-way analysis of variance. Overall, cortical thickness peaked in childhood and had a steep decrease during the first 2-3 decades of life; thereafter, it showed a gradual monotonic decrease which was steeper in men than in women particularly in middle-life. Notable exceptions to this general pattern were entorhinal, temporopolar and anterior cingulate cortices. Inter-individual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results reconcile uncertainties about age-related trajectories of cortical thickness; the centile values provide estimates of normative variance in cortical thickness, and may assist in detecting abnormal deviations in cortical thickness, and associated behavioural, cognitive and clinical outcomes.

scholarly journals Graphing Ecotoxicology: The MAGIC Graph for Linking Environmental Data on Chemicals

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 34 ◽  
Author(s):  
Sascha Bub ◽  
Jakob Wolfram ◽  
Sebastian Stehle ◽  
Lara Petschick ◽  
Ralf Schulz
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Environmental Data ◽  
Relational Data ◽  
Chemical Information ◽  
Graph Database ◽  
Data Systems ◽  
Large Scale Analysis ◽  
Multiple Data ◽  
The Impact

Assessing the impact of chemicals on the environment and addressing subsequent issues are two central challenges to their safe use. Environmental data are continuously expanding, requiring flexible, scalable, and extendable data management solutions that can harmonize multiple data sources with potentially differing nomenclatures or levels of specificity. Here, we present the methodological steps taken to construct a rule-based labeled property graph database, the “Meta-analysis of the Global Impact of Chemicals” (MAGIC) graph, for potential environmental impact chemicals (PEIC) and its subsequent application harmonizing multiple large-scale databases. The resulting data encompass 16,739 unique PEICs attributed to their corresponding chemical class, stereo-chemical information, valid synonyms, use types, unique identifiers (e.g., Chemical Abstract Service registry number CAS RN), and others. These data provide researchers with additional chemical information for a large amount of PEICs and can also be publicly accessed using a web interface. Our analysis has shown that data harmonization can increase up to 98% when using the MAGIC graph approach compared to relational data systems for datasets with different nomenclatures. The graph database system and its data appear more suitable for large-scale analysis where traditional (i.e., relational) data systems are reaching conceptional limitations.

scholarly journals Genevestigator V3: A Reference Expression Database for the Meta-Analysis of Transcriptomes

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Tomas Hruz ◽  
Oliver Laule ◽  
Gabor Szabo ◽  
Frans Wessendorp ◽  
Stefan Bleuler ◽  
...  
Keyword(s):  
Data Storage ◽  
Regulatory Networks ◽  
Large Scale ◽  
Meta Analysis ◽  
Software Tool ◽  
Manual Curation ◽  
Discovery Research ◽  
Expression Of Genes ◽  
Computationally Intensive ◽  
Analysis System

The Web-based software tool Genevestigator provides powerful tools for biologists to explore gene expression across a wide variety of biological contexts. Its first releases, however, were limited by the scaling ability of the system architecture, multiorganism data storage and analysis capability, and availability of computationally intensive analysis methods. Genevestigator V3 is a novel meta-analysis system resulting from new algorithmic and software development using a client/server architecture, large-scale manual curation and quality control of microarray data for several organisms, and curation of pathway data for mouse and Arabidopsis. In addition to improved querying features, Genevestigator V3 provides new tools to analyze the expression of genes in many different contexts, to identify biomarker genes, to cluster genes into expression modules, and to model expression responses in the context of metabolic and regulatory networks. Being a reference expression database with user-friendly tools, Genevestigator V3 facilitates discovery research and hypothesis validation.

scholarly journals Comparison of Merging and Meta-Analysis as Alternative Approaches for Integrative Gene Expression Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jonatan Taminau ◽  
Cosmin Lazar ◽  
Stijn Meganck ◽  
Ann Nowé
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Meta Analysis ◽  
Microarray Gene Expression Data ◽  
Data Sets ◽  
Expression Data ◽  
Scale Analysis ◽  
Microarray Gene Expression ◽  
Large Scale Analysis ◽  
Microarray Gene

An increasing amount of microarray gene expression data sets is available through public repositories. Their huge potential in making new findings is yet to be unlocked by making them available for large-scale analysis. In order to do so it is essential that independent studies designed for similar biological problems can be integrated, so that new insights can be obtained. These insights would remain undiscovered when analyzing the individual data sets because it is well known that the small number of biological samples used per experiment is a bottleneck in genomic analysis. By increasing the number of samples the statistical power is increased and more general and reliable conclusions can be drawn. In this work, two different approaches for conducting large-scale analysis of microarray gene expression data—meta-analysis and data merging—are compared in the context of the identification of cancer-related biomarkers, by analyzing six independent lung cancer studies. Within this study, we investigate the hypothesis that analyzing large cohorts of samples resulting in merging independent data sets designed to study the same biological problem results in lower false discovery rates than analyzing the same data sets within a more conservative meta-analysis approach.

scholarly journals Genetic links between endometriosis and cancers in women

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8135 ◽  
Author(s):  
Salma Begum Bhyan ◽  
Li Zhao ◽  
YongKiat Wee ◽  
Yining Liu ◽  
Min Zhao
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Relationships ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Expression Of Genes ◽  
Genetic Mechanisms

Endometriosis is a chronic disease occurring during the reproductive stage of women. Although there is only limited association between endometriosis and gynecological cancers with regard to clinical features, the molecular basis of the relationship between these diseases is unexplored. We conducted a systematic study by integrating literature-based evidence, gene expression and large-scale cancer genomics data in order to reveal any genetic relationships between endometriosis and cancers in women. We curated 984 endometriosis-related genes from 3270 PubMed articles and then conducted a meta-analysis of the two public gene expression profiles related to endometriosis which identified Differential Expression of Genes (DEGs). Following an overlapping analysis, we identified 39 key endometriosis-related genes common in both literature and DEG analysis. Finally, the functional analysis confirmed that all the 39 genes were associated with the vital processes of tumour formation and cancer progression and that two genes (PGR and ESR1) were common to four cancers of women. From network analysis, we identified a novel linker gene, C3AR1, which had not been implicated previously in endometriosis. The shared genetic mechanisms of endometriosis and cancers in women identified in this study provided possible new avenues of multiple disease management and treatments through early diagnosis.

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