Abstract 1356: Meta-analysis and lack of independence assumption: Application in biomarker discovery

2021 ◽  
Author(s):  
Farnoosh Abbas-Aghababazadeh ◽  
Benjamin Haibe-Kains
Keyword(s):  
Biomarker Discovery ◽  
Meta Analysis ◽  
Independence Assumption

scholarly journals Epigenome-wide meta-analysis of DNA methylation differences in prefrontal cortex implicates the immune processes in Alzheimer’s disease

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Lanyu Zhang ◽  
Tiago C. Silva ◽  
Juan I. Young ◽  
Lissette Gomez ◽  
Michael A. Schmidt ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dna Methylation ◽  
Prefrontal Cortex ◽  
Biomarker Discovery ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Braak Stage ◽  
Polycomb Repressive Complex 2 ◽  
Differentially Methylated Genes

AbstractDNA methylation differences in Alzheimer’s disease (AD) have been reported. Here, we conducted a meta-analysis of more than 1000 prefrontal cortex brain samples to prioritize the most consistent methylation differences in multiple cohorts. Using a uniform analysis pipeline, we identified 3751 CpGs and 119 differentially methylated regions (DMRs) significantly associated with Braak stage. Our analysis identified differentially methylated genes such as MAMSTR, AGAP2, and AZU1. The most significant DMR identified is located on the MAMSTR gene, which encodes a cofactor that stimulates MEF2C. Notably, MEF2C cooperates with another transcription factor, PU.1, a central hub in the AD gene network. Our enrichment analysis highlighted the potential roles of the immune system and polycomb repressive complex 2 in pathological AD. These results may help facilitate future mechanistic and biomarker discovery studies in AD.

scholarly journals Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: findings from the ENIGMA Epigenetics Working Group

2019 ◽  
Author(s):  
Tianye Jia ◽  
Congying Chu ◽  
Yun Liu ◽  
Jenny van Dongen ◽  
Evangelos Papastergios ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Nucleus Accumbens ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Association Studies ◽  
Meta Analysis ◽  
Hippocampal Volume ◽  
Small Sample ◽  
Small Sample Sizes ◽  
Meta Analyses

AbstractDNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)—three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

Meta-Analyses as a Multi-Level Model

2019 ◽  
pp. 109442811985747
Author(s):  
Janaki Gooty ◽  
George C. Banks ◽  
Andrew C. Loignon ◽  
Scott Tonidandel ◽  
Courtney E. Williams
Keyword(s):  
Educational Psychology ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Independence Assumption ◽  
The Social ◽  
Level Model ◽  
Multi Level ◽  
Meta Analyses ◽  
User Friendly ◽  
Organizational Sciences

Meta-analyses are well known and widely implemented in almost every domain of research in management as well as the social, medical, and behavioral sciences. While this technique is useful for determining validity coefficients (i.e., effect sizes), meta-analyses are predicated on the assumption of independence of primary effect sizes, which might be routinely violated in the organizational sciences. Here, we discuss the implications of violating the independence assumption and demonstrate how meta-analysis could be cast as a multilevel, variance known (Vknown) model to account for such dependency in primary studies’ effect sizes. We illustrate such techniques for meta-analytic data via the HLM 7.0 software as it remains the most widely used multilevel analyses software in management. In so doing, we draw on examples in educational psychology (where such techniques were first developed), organizational sciences, and a Monte Carlo simulation (Appendix). We conclude with a discussion of implications, caveats, and future extensions. Our Appendix details features of a newly developed application that is free (based on R), user-friendly, and provides an alternative to the HLM program.

scholarly journals Proteomic landscape of Alzheimer’s Disease: novel insights into pathogenesis and biomarker discovery

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Bing Bai ◽  
David Vanderwall ◽  
Yuxin Li ◽  
Xusheng Wang ◽  
Suresh Poudel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rna Splicing ◽  
Biomarker Discovery ◽  
Meta Analysis ◽  
Treatment Strategies ◽  
Cell Types ◽  
Synaptic Function ◽  
Mitochondrial Activity ◽  
Cellular Markers

AbstractMass spectrometry-based proteomics empowers deep profiling of proteome and protein posttranslational modifications (PTMs) in Alzheimer’s disease (AD). Here we review the advances and limitations in historic and recent AD proteomic research. Complementary to genetic mapping, proteomic studies not only validate canonical amyloid and tau pathways, but also uncover novel components in broad protein networks, such as RNA splicing, development, immunity, membrane transport, lipid metabolism, synaptic function, and mitochondrial activity. Meta-analysis of seven deep datasets reveals 2,698 differentially expressed (DE) proteins in the landscape of AD brain proteome (n = 12,017 proteins/genes), covering 35 reported AD genes and risk loci. The DE proteins contain cellular markers enriched in neurons, microglia, astrocytes, oligodendrocytes, and epithelial cells, supporting the involvement of diverse cell types in AD pathology. We discuss the hypothesized protective or detrimental roles of selected DE proteins, emphasizing top proteins in “amyloidome” (all biomolecules in amyloid plaques) and disease progression. Comprehensive PTM analysis represents another layer of molecular events in AD. In particular, tau PTMs are correlated with disease stages and indicate the heterogeneity of individual AD patients. Moreover, the unprecedented proteomic coverage of biofluids, such as cerebrospinal fluid and serum, procures novel putative AD biomarkers through meta-analysis. Thus, proteomics-driven systems biology presents a new frontier to link genotype, proteotype, and phenotype, accelerating the development of improved AD models and treatment strategies.

scholarly journals What is the test-retest reliability of common task-fMRI measures? New empirical evidence and a meta-analysis

2019 ◽  
Author(s):  
Maxwell L. Elliott ◽  
Annchen R. Knodt ◽  
David Ireland ◽  
Meriwether L. Morris ◽  
Richie Poulton ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Disease Risk ◽  
Brain Activity ◽  
Meta Analysis ◽  
A Priori ◽  
Common Task ◽  
Human Connectome Project ◽  
State Of Affairs ◽  
Test Retest Reliability ◽  
Overall Reliability

AbstractIdentifying brain biomarkers of disease risk is a growing priority in neuroscience. The ability to identify meaningful biomarkers is limited by measurement reliability; unreliable measures are unsuitable for predicting clinical outcomes. Measuring brain activity using task-fMRI is a major focus of biomarker development; however, the reliability of task-fMRI has not been systematically evaluated. We present converging evidence demonstrating poor reliability of task-fMRI measures. First, a meta-analysis of 90 experiments (N=1,008) revealed poor overall reliability (mean ICC=.397). Second, the test-retest reliabilities of activity in a priori regions of interest across 11 common fMRI tasks collected in the context of the Human Connectome Project (N=45) and the Dunedin Study (N=20) were poor (ICCs=.067-.485). Collectively, these findings demonstrate that common task-fMRI measures are not currently suitable for brain biomarker discovery or individual differences research. We review how this state of affairs came to be and highlight avenues for improving task-fMRI reliability.

scholarly journals What Is the Test-Retest Reliability of Common Task-Functional MRI Measures? New Empirical Evidence and a Meta-Analysis

2020 ◽  
Vol 31 (7) ◽  
pp. 792-806 ◽  
Author(s):  
Maxwell L. Elliott ◽  
Annchen R. Knodt ◽  
David Ireland ◽  
Meriwether L. Morris ◽  
Richie Poulton ◽  
...  
Keyword(s):  
Functional Mri ◽  
Biomarker Discovery ◽  
Disease Risk ◽  
Brain Activity ◽  
Meta Analysis ◽  
A Priori ◽  
Intraclass Correlation ◽  
Common Task ◽  
Human Connectome Project ◽  
State Of Affairs

Identifying brain biomarkers of disease risk is a growing priority in neuroscience. The ability to identify meaningful biomarkers is limited by measurement reliability; unreliable measures are unsuitable for predicting clinical outcomes. Measuring brain activity using task functional MRI (fMRI) is a major focus of biomarker development; however, the reliability of task fMRI has not been systematically evaluated. We present converging evidence demonstrating poor reliability of task-fMRI measures. First, a meta-analysis of 90 experiments ( N = 1,008) revealed poor overall reliability—mean intraclass correlation coefficient (ICC) = .397. Second, the test-retest reliabilities of activity in a priori regions of interest across 11 common fMRI tasks collected by the Human Connectome Project ( N = 45) and the Dunedin Study ( N = 20) were poor (ICCs = .067–.485). Collectively, these findings demonstrate that common task-fMRI measures are not currently suitable for brain biomarker discovery or for individual-differences research. We review how this state of affairs came to be and highlight avenues for improving task-fMRI reliability.

scholarly journals MetaMSD: meta analysis for mass spectrometry data

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6699
Author(s):  
So Young Ryu ◽  
George A. Wendt
Keyword(s):  
Mass Spectrometry ◽  
Biomarker Discovery ◽  
Meta Analysis ◽  
Simulated Data ◽  
Mass Spectrometry Data ◽  
Transplant Patients ◽  
Proteomic Data ◽  
Differential Proteins ◽  
Command Line Tool ◽  
Kidney Transplant Patients

Mass spectrometry-based proteomics facilitate disease understanding by providing protein abundance information about disease progression. For the same type of disease studies, multiple mass spectrometry datasets may be generated. Integrating multiple mass spectrometry datasets can provide valuable information that a single dataset analysis cannot provide. In this article, we introduce a meta-analysis software, MetaMSD (Meta Analysis for Mass Spectrometry Data) that is specifically designed for mass spectrometry data. Using Stouffer’s or Pearson’s test, MetaMSD detects significantly more differential proteins than the analysis based on the single best experiment. We demonstrate the performance of MetaMSD using simulated data, urinary proteomic data of kidney transplant patients, and breast cancer proteomic data. Noting the common practice of performing a pilot study prior to a main study, this software will help proteomics researchers fully utilize the benefit of multiple studies (or datasets), thus optimizing biomarker discovery. MetaMSD is a command line tool that automatically outputs various graphs and differential proteins with confidence scores. It is implemented in R and is freely available for public use at https://github.com/soyoungryu/MetaMSD. The user manual and data are available at the site. The user manual is written in such a way that scientists who are not familiar with R software can use MetaMSD.

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