scholarly journals Meta-analytic Gaussian Network Aggregation

Psychometrika ◽  
2021 ◽  
Author(s):  
Sacha Epskamp ◽  
Adela-Maria Isvoranu ◽  
Mike W.-L. Cheung
Keyword(s):  
Graphical Models ◽  
Fixed Effects ◽  
Large Scale ◽  
Meta Analysis ◽  
Correlation Coefficients ◽  
Likelihood Estimation ◽  
Post Traumatic Stress ◽  
Modeling Framework ◽  
Network Aggregation ◽  
Gaussian Network

AbstractA growing number of publications focus on estimating Gaussian graphical models (GGM, networks of partial correlation coefficients). At the same time, generalizibility and replicability of these highly parameterized models are debated, and sample sizes typically found in datasets may not be sufficient for estimating the underlying network structure. In addition, while recent work emerged that aims to compare networks based on different samples, these studies do not take potential cross-study heterogeneity into account. To this end, this paper introduces methods for estimating GGMs by aggregating over multiple datasets. We first introduce a general maximum likelihood estimation modeling framework in which all discussed models are embedded. This modeling framework is subsequently used to introduce meta-analytic Gaussian network aggregation (MAGNA). We discuss two variants: fixed-effects MAGNA, in which heterogeneity across studies is not taken into account, and random-effects MAGNA, which models sample correlations and takes heterogeneity into account. We assess the performance of MAGNA in large-scale simulation studies. Finally, we exemplify the method using four datasets of post-traumatic stress disorder (PTSD) symptoms, and summarize findings from a larger meta-analysis of PTSD symptom.

scholarly journals Meta-analytic Gaussian Network Aggregation

2020 ◽  
Author(s):  
Sacha Epskamp ◽  
Adela-Maria Isvoranu ◽  
Mike W.-L. Cheung
Keyword(s):  
Graphical Models ◽  
Fixed Effects ◽  
Large Scale ◽  
Correlation Coefficients ◽  
Likelihood Estimation ◽  
Post Traumatic Stress ◽  
Modeling Framework ◽  
Network Aggregation ◽  
Study Heterogeneity ◽  
Gaussian Network

A growing number of publications focuses on estimating Gaussian graphical models (GGMs, networks of partial correlation coefficients). At the same time, generalizibility and replicability of these highly parameterized models are debated, and sample sizes typically found in datasets may not be sufficient for estimating the underlying network structure. In addition, while recent work emerged that aims to compare networks based on different samples, these studies do not take potential cross-study heterogeneity into account. To this end, this paper introduces methods for estimating GGMs through aggregating over multiple datasets. We first introduce a general maximum likelihood estimation modeling framework in which all discussed models are embedded. This modeling framework is subsequently used to introduce meta-analytic Gaussian network aggregation (MAGNA). We discuss two variants: fixed-effects MAGNA, in which heterogeneity across studies is not taken into account, and random-effects MAGNA, which models sample correlations and takes heterogeneity into account. We exemplify the method using four datasets of post-traumatic stress disorder symptoms, as well as one large dataset of depression, anxiety and stress symptoms. Finally, we assess the performance of MAGNA in large-scale simulation studies.

scholarly journals ENIGMA and Global Neuroscience: A Decade of Large-Scale Studies of the Brain in Health and Disease across more than 40 Countries

2019 ◽  
Author(s):  
Paul Thompson ◽  
Neda Jahanshad ◽  
Christopher R. K. Ching ◽  
Lauren Salminen ◽  
Sophia I Thomopoulos ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
22Q11.2 Deletion Syndrome ◽  
Autism Spectrum ◽  
Deletion Syndrome ◽  
Sex And Gender ◽  
Post Traumatic Stress ◽  
Global Alliance ◽  
Compulsive Disorder ◽  
Health And Disease

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1,400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive and psychosocial factors.

The Association between COMT Val158Met Polymorphism and the Post-Traumatic Stress Disorder Risk: A Meta-Analysis

2021 ◽  
pp. 1-14
Author(s):  
Mi Su ◽  
Yongyan Song
Keyword(s):  
Post Traumatic Stress Disorder ◽  
Traumatic Stress ◽  
Large Scale ◽  
Stress Disorder ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Post Traumatic Stress ◽  
Val158met Polymorphism ◽  
Post Traumatic ◽  
The Impact

<b><i>Background:</i></b> Genetic factors were suggested to have influence on the development of post-traumatic stress disorder (PTSD). The possible association between catechol-O-methyltransferase (<i>COMT</i>) Val158Met polymorphism and PTSD has been evaluated in several studies. But the results were still controversial. Therefore, we conduct this meta-analysis to address these issues. <b><i>Methods:</i></b> The PubMed, EMBASE, Cochrane Library, and Web of Science databases were searched for eligible studies. The pooled odds ratio (OR) with 95% confidence interval (CI) was calculated to estimate the association between <i>COMT</i> Val158Met polymorphism and PTSD. <b><i>Results:</i></b> Five articles including 6 studies with 893 cases and 968 controls were finally included in the present meta-analysis. The pooled analyses did not demonstrate a significant association between the <i>COMT</i> Val158Met polymorphism and PTSD in any of the selected genetic models: allele model (OR = 1.13, 95% CI: 0.97–1.31), dominant model (OR = 1.17, 95% CI: 0.93–1.46), recessive model (OR = 1.44, 95% CI: 0.78–2.66), and additive model (OR = 1.54, 95% CI: 0.85–2.80). Subgroup analyses suggested that the Hardy-Weinberg equilibrium status of genotype distributions could influence the relationship of <i>COMT</i> Val158Met polymorphism and PTSD. <b><i>Conclusions:</i></b> The present meta-analysis suggested that the <i>COMT</i> Val158Met polymorphism may not be associated with the PTSD risk. Further large-scale and population-representative studies are warranted to evaluate the impact of the <i>COMT</i> Val158Met polymorphism on the risk of PTSD.

scholarly journals The association between environmental exposures to chlordanes, adiposity and diabetes-related features: a systematic review and meta-analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vânia Mendes ◽  
Cláudia Ribeiro ◽  
Inês Delgado ◽  
Bárbara Peleteiro ◽  
Martine Aggerbeck ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Meta Analysis ◽  
Scientific Evidence ◽  
Endocrine Disrupting Chemicals ◽  
Correlation Coefficients ◽  
Epidemiological Data ◽  
Original Data ◽  
Regression Coefficients ◽  
Cross Sectional ◽  
Hormonal Function

AbstractChlordane compounds (CHLs) are components of technical chlordane listed in the Stockholm convention on persistent organic pollutants identified as endocrine disrupting chemicals (EDCs) and may interfere with hormone biosynthesis, metabolism or action resulting in an unbalanced hormonal function. There is increasing scientific evidence showing EDCs as risk factors in the pathogenesis and development of obesity and obesity-related metabolic syndromes such as type 2 diabetes, but there is no systematized information on the effect of CHLs in humans. Our aim is to identify the epidemiological data on the association between CHLs with adiposity and diabetes using a systematic approach to identify the available data and summarizing the results through meta-analysis. We searched PubMed and Web of Science from inception up to 15 February 2021, to retrieve original data on the association between chlordanes, and adiposity or diabetes. For adiposity, regression coefficients and Pearson or Spearman correlation coefficients were extracted and converted into standardized regression coefficients. Data were combined using fixed effects meta-analyses to compute summary regression coefficients and corresponding 95% confidence intervals (95% CI). For the association between chlordanes and diabetes, Odds ratios (ORs) were extracted and the DerSimonian and Laird method was used to compute summary estimates and respective 95% CI. For both, adjusted estimates were preferred, whenever available. Among 31 eligible studies, mostly using a cross-sectional approach, the meta-analysis for adiposity was possible only for oxychlordane and transchlordane, none of them were significantly associated with adiposity [(β = 0.04, 95% CI 0.00; 0.07, I2 = 89.7%)] and (β = 0.02, 95% CI − 0.01; 0.06), respectively. For diabetes, the estimates were positive for all compounds but statistically significant for oxychlordane [OR = 1.96 (95% CI 1.19; 3.23)]; for trans-nonachlor [OR = 2.43 (95% CI 1.64; 3.62)] and for heptachlor epoxide [OR = 1.88 (95% CI 1.42; 2.49)]. Our results support that among adults, the odds of having diabetes significantly increase with increasing levels of chlordanes. The data did not allow to reach a clear conclusion regarding the association with adiposity.

scholarly journals Irreversible Electroporation in Patients with Pancreatic Cancer: How Important Is the New Weapon?

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Guo Tian ◽  
Xueping Liu ◽  
Qiyu Zhao ◽  
Danxia Xu ◽  
Tian’an Jiang
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Fixed Effects ◽  
Large Scale ◽  
Meta Analysis ◽  
Irreversible Electroporation ◽  
Complete Response ◽  
Cochrane Library ◽  
Pooled Prevalence ◽  
Secondary Outcomes

Background. Pancreatic cancer (PC) is a deadly disease with poor prognosis in the general population. We aimed to quantitate overall survival of patients with PC after irreversible electroporation (IRE) and the incidence of relevant complications. Methods. We performed a literature search via five electronic databases (PubMed, Embase, Web of Science, Scopus, and Cochrane Library databases) up to August 2017. The primary outcomes were overall survival and prognosis. Secondary outcomes included the response of post-IRE complications. Fixed-effects or random-effects meta-analysis was conducted to pool these data. Results. A total of 15 eligible articles involving 535 patients were included. The primary outcomes showed that the pooled prevalence estimates of overall survival were 94.1% (95% CI: 90.7–97.5), 80.9% (95% CI: 72.5–89.4), 54.5% (95% CI: 38.3–70.6), and 33.8% (95% CI: 14.2–53.5) at 3, 6, 12, and 24 months, and the pooled prevalence data of complete response (CR) at 2 months, partial response (PR) at 3 months, and progression at 3 months were 12.5% (95% CI: 2.9–22.2), 48.5% (95% CI: 39.4–57.6), and 19.7% (95% CI: 7.3–32.2), respectively. The secondary outcomes showed that the pooled prevalence values of post-IRE complications were abscess 6.6% (95% CI: 0.2–13), fistula 10.6% (95% CI: 2.5–18.7), pain 33.5% (95% CI: 14.5–52.5), infection 16.1% (95% CI: 3.9–28.4), thrombosis 4.9% (95% CI: 1.2–8.5), pancreatitis 7.2% (95% CI: 3.1–11.2), bleeding 4.2% (95% CI: −0.5–8.9), cholangitis 4.2% (95% CI: −0.5–8.9), nausea 9.6% (95% CI: 4.4–14.8), biliary obstruction 13.8% (95% CI: 4.2–23.3), chest tightness 7.6% (95% CI: 0.5–14.6), and hypoglycemia 5.9% (95% CI: −0.4–12.2). Conclusions. This meta-analysis indicated a clear survival benefit for PC patients who received irreversible electroporation therapy, although future safety and effectivity monitoring from more large-scale studies will be needed.

scholarly journals Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study

2021 ◽  
Author(s):  
Mohammed Saqr ◽  
Sonsoles López-Pernas
Keyword(s):  
Academic Achievement ◽  
Academic Success ◽  
Collaborative Learning ◽  
Information Exchange ◽  
Large Scale ◽  
Meta Analysis ◽  
Correlation Coefficients ◽  
Centrality Measures ◽  
Computer Supported Collaborative Learning ◽  
Student Role

AbstractThis study empirically investigates diffusion-based centralities as depictions of student role-based behavior in information exchange, uptake and argumentation, and as consistent indicators of student success in computer-supported collaborative learning. The analysis is based on a large dataset of 69 courses (n = 3,277 students) with 97,173 total interactions (of which 8,818 were manually coded). We examined the relationship between students’ diffusion-based centralities and a coded representation of their interactions in order to investigate the extent to which diffusion-based centralities are able to adequately capture information exchange and uptake processes. We performed a meta-analysis to pool the correlation coefficients between centralities and measures of academic achievement across all courses while considering the sample size of each course. Lastly, from a cluster analysis using students’ diffusion-based centralities aimed at discovering student role-taking within interactions, we investigated the validity of the discovered roles using the coded data. There was a statistically significant positive correlation that ranged from moderate to strong between diffusion-based centralities and the frequency of information sharing and argumentation utterances, confirming that diffusion-based centralities capture important aspects of information exchange and uptake. The results of the meta-analysis showed that diffusion-based centralities had the highest and most consistent combined correlation coefficients with academic achievement as well as the highest predictive intervals, thus demonstrating their advantage over traditional centrality measures. Characterizations of student roles based on diffusion centralities were validated using qualitative methods and were found to meaningfully relate to academic performance. Diffusion-based centralities are feasible to calculate, implement and interpret, while offering a viable solution that can be deployed at any scale to monitor students’ productive discussions and academic success.

scholarly journals The Neural Basis of Motivational Influences on Cognitive Control

2017 ◽  
Author(s):  
Cameron Parro ◽  
Matthew L Dixon ◽  
Kalina Christoff
Keyword(s):  
Cognitive Control ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Premotor Cortex ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Control Mechanisms ◽  
Neural Basis ◽  
The Brain

AbstractCognitive control mechanisms support the deliberate regulation of thought and behavior based on current goals. Recent work suggests that motivational incentives improve cognitive control, and has begun to elucidate the brain regions that may support this effect. Here, we conducted a quantitative meta-analysis of neuroimaging studies of motivated cognitive control using activation likelihood estimation (ALE) and Neurosynth in order to delineate the brain regions that are consistently activated across studies. The analysis included functional neuroimaging studies that investigated changes in brain activation during cognitive control tasks when reward incentives were present versus absent. The ALE analysis revealed consistent recruitment in regions associated with the frontoparietal control network including the inferior frontal sulcus (IFS) and intraparietal sulcus (IPS), as well as consistent recruitment in regions associated with the salience network including the anterior insula and anterior mid-cingulate cortex (aMCC). A large-scale exploratory meta-analysis using Neurosynth replicated the ALE results, and also identified the caudate nucleus, nucleus accumbens, medial thalamus, inferior frontal junction/premotor cortex (IFJ/PMC), and hippocampus. Finally, we conducted separate ALE analyses to compare recruitment during cue and target periods, which tap into proactive engagement of rule-outcome associations, and the mobilization of appropriate viscero-motor states to execute a response, respectively. We found that largely distinct sets of brain regions are recruited during cue and target periods. Altogether, these findings suggest that flexible interactions between frontoparietal, salience, and dopaminergic midbrain-striatal networks may allow control demands to be precisely tailored based on expected value.

Abstract MP84: Trans-ethnic Meta-analysis of Exome Chip Data Reveals Novel Low-frequency Variants Contributing to Central Adiposity

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
Kristin L Young ◽  
Anne Justice ◽  
Tugce Karaderi ◽  
Heather Highland ◽  
Mariaelisa Graff ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Large Scale ◽  
Blood Glucose Concentration ◽  
Meta Analysis ◽  
Low Frequency ◽  
Fat Distribution ◽  
Central Adiposity ◽  
Single Nucleotide Variants ◽  
Chip Data ◽  
Exome Chip

Central adiposity is a leading risk factor for cardiovascular disease, and genetic factors contribute both to fat distribution, measured as waist-to-hip ratio adjusted for BMI (WHRa), and to differences in central adiposity prevalence. To date, 49 loci have been associated with WHRa, based on studies of common [minor allele frequency (MAF) ≥5%] single nucleotide variants (SNVs), primarily in European descent populations. Our aim was to identify low frequency (LFV: MAF <5%) and rare (RV: MAF <1%) coding variants associated with WHRa using Exome-Chip data from 344,369 individuals of European (84%), South Asian (8%), African (5%), East Asian(2%), and Hispanic/Latino (1%) ancestry. We performed fixed effects meta-analyses of study-specific WHRa associations stratified by sex and ancestry and then combined across strata for both SNV and gene-based results. We used a strict definition of variants annotated as damaging by 5 algorithms to perform gene-based analyses using the sequence kernel association test (SKAT). Analyses included up to 284,499 SNVs (218,195 with MAF<5%), and 15,063 genes with at least one SNV that met our inclusion criteria. Five LFVs reached chip-wide significance (CWS: P<2.5E-7) in our all ancestry sex-combined analyses, including one novel non-synonymous LFV in RAPGEF3 [MAF=0.01, β (SE) = -0.09 (0.012), P=1.28E-13]. In addition, one novel RV reached CWS in men for UGGT2 [MAF<0.01, β (SE) = -0.142 (0.025), P=9.71E-9], and one RV reached CWS in women for ACVR1C [MAF<0.01, β (SE) = -0.09 (0.018), P=1.09E-7]. Gene-based analyses identified RAPGEF3 (P=1.18E-11) as significantly associated with WHRa in the all ancestry sex combined analyses after correction for multiple tests (P<2.5E-6), though conditional analysis revealed that this result is driven by the top SV identified in this region. RAPGEF3 also shows a significant association (p=4.68E-12) in all ancestry, sex combined gene-based analysis of BMI. RAPGEF3 is expressed in subcutaneous and visceral adipose tissue, and has been implicated in insulin regulation. RAPGEF3 plays a role in the GLP1 pathway, which controls insulin secretion in response to blood glucose concentration. Our results highlight the importance of large-scale genomic studies for identifying LFV and RV influencing central fat distribution. Understanding these genetic effects may provide insights into the progression of central adiposity and highlight potential population-specific variants that increase susceptibility.

scholarly journals Meta-Analysis to Determine the Effects of Plant Disease Management Measures: Review and Case Studies on Soybean and Apple

2011 ◽  
Vol 101 (1) ◽  
pp. 31-41 ◽  
Author(s):  
Henry K. Ngugi ◽  
Paul D. Esker ◽  
Harald Scherm
Keyword(s):  
Disease Management ◽  
Case Studies ◽  
Fixed Effects ◽  
Large Scale ◽  
Plant Disease ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Quantitative Evidence ◽  
Plant Disease Management ◽  
Meta Analyses

The continuing exponential increase in scientific knowledge, the growing availability of large databases containing raw or partially annotated information, and the increased need to document impacts of large-scale research and funding programs provide a great incentive for integrating and adding value to previously published (or unpublished) research through quantitative synthesis. Meta-analysis has become the standard for quantitative evidence synthesis in many disciplines, offering a broadly accepted and statistically powerful framework for estimating the magnitude, consistency, and homogeneity of the effect of interest across studies. Here, we review previous and current uses of meta-analysis in plant pathology with a focus on applications in epidemiology and disease management. About a dozen formal meta-analyses have been published in the plant pathological literature in the past decade, and several more are currently in progress. Three broad research questions have been addressed, the most common being the comparative efficacy of chemical treatments for managing disease and reducing yield loss across environments. The second most common application has been the quantification of relationships between disease intensity and yield, or between different measures of disease, across studies. Lastly, meta-analysis has been applied to assess factors affecting pathogen–biocontrol agent interactions or the effectiveness of biological control of plant disease or weeds. In recent years, fixed-effects meta-analysis has been largely replaced by random- (or mixed-) effects analysis owing to the statistical benefits associated with the latter and the wider availability of computer software to conduct these analyses. Another recent trend has been the more common use of multivariate meta-analysis or meta-regression to analyze the impacts of study-level independent variables (moderator variables) on the response of interest. The application of meta-analysis to practical problems in epidemiology and disease management is illustrated with case studies from our work on Phakopsora pachyrhizi on soybean and Erwinia amylovora on apple. We show that although meta-analyses are often used to corroborate and validate general conclusions drawn from more traditional, qualitative reviews, they can also reveal new patterns and interpretations not obvious from individual studies.

scholarly journals GWAS of QRS Duration Identifies New Loci Specific to Hispanic/Latino Populations Swenson Hispanic/Latino QRS GWAS

2018 ◽  
Author(s):  
Brenton R. Swenson ◽  
Tin Louie ◽  
Henry J. Lin ◽  
Raú MéndezGiráldez ◽  
Jennifer E Below ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Integral Membrane Protein ◽  
Genome Wide Association Studies ◽  
Significance Threshold ◽  
Qrs Prolongation ◽  
Qrs Duration ◽  
Failure Prognosis

ABSTRACTBackgroundThe electrocardiographically quantified QRS duration measures ventricular depolarization and conduction. QRS prolongation has been associated with poor heart failure prognosis and cardiovascular mortality, including sudden death. While previous genome-wide association studies (GWAS) have identified 32 QRS SNPs across 26 loci among European, African, and Asian-descent populations, the genetics of QRS among Hispanics/Latinos has not been previously explored.MethodsWe performed a GWAS of QRS duration among Hispanic/Latino ancestry populations (n=15,124) from four studies using 1000 Genomes imputed genotype data (adjusted for age, sex, global ancestry, clinical and study-specific covariates). Study-specific results were combined using fixed-effects, inverse variance-weighted meta-analysis.ResultsWe identified six loci associated with QRS (P<5×10−8), including two novel loci: MYOCD, a nuclear protein expressed in the heart, and SYT1, an integral membrane protein. The top association in the MYOCD locus, intronic SNP rs16946539, was found in Hispanics/Latinos with a minor allele frequency (MAF) of 0.04, but is monomorphic in European and African descent populations. The most significant QRS duration association was for intronic SNP rs3922344 (P= 8.56×10−26) in SCN5A/SCN10A. Three additional previously identified loci, CDKN1A, VTI1A, and HAND1, also exceeded the GWAS significance threshold among Hispanics/Latinos. A total of 27 of 32 previously identified QRS duration SNPs were shown to generalize in Hispanics/Latinos.ConclusionsOur QRS duration GWAS, the first in Hispanic/Latino populations, identified two new loci, underscoring the utility of extending large scale genomic studies to currently under-examined populations.

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