EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data (2025)

AbstractSummary: The R package EasyStrata facilitates the evaluation and visualization of stratified genome-wide association meta-analyses (GWAMAs) results. It provides (i) statistical methods to test and account for between-strata difference as a means to tackle gene–strata interaction effects and (ii) extended graphical features tailored for stratified GWAMA results. The software provides further features also suitable for general GWAMAs including functions to annotate, exclude or highlight specific loci in plots or to extract independent subsets of loci from genome-wide datasets. It is freely available and includes a user-friendly scripting interface that simplifies data handling and allows for combining statistical and graphical functions in a flexible fashion.Availability: EasyStrata is available for free (under the GNU General Public License v3) from our Web site www.genepi-regensburg.de/easystrata and from the CRAN R package repository cran.r-project.org/web/packages/EasyStrata/.Contact: thomas.winkler@ukr.de or iris.heid@ukr.deSupplementary information: Supplementary data are available at Bioinformatics online.

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EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data (2025)

FAQs

What is meta-analysis of genome-wide datasets? ›

Meta-analysis allows the exploration of the extent and reasons for heterogeneity of the genetic effects across datasets, besides providing summary effects. A wide array of methods may be used, including fixed effects, random effects, and Bayesian meta-analysis and they have particular advantages and disadvantages.

What is a GWAS meta-analysis? ›

Meta-analysis of many genome-wide association studies improves the power to detect more associations, and to investigate the consistency or heterogeneity of these associations across diverse datasets and study populations.

What is meta-analysis in genetics? ›

Meta-analysis solves a problem associated with genetic association studies by analyzing variation in the results of different studies by identifying inter-study heterogeneity.

What is genome-wide association Analyses? ›

A genome-wide association study (GWAS) is an approach to compare the genomes from many different people to find genetic markers associated with a particular phenotype or risk of disease.

What does a meta-analysis tell you? ›

Meta-analysis findings may not only be quantitative but also may be qualitative and reveal the biases, strengths, and weaknesses of existing studies. The results of a meta-analysis can be used to form treatment recommendations or to provide guidance in the design of future clinical trials.

What are the three types of meta-analysis? ›

There are four widely used methods of meta-analysis for dichotomous outcomes, three fixed-effect methods (Mantel-Haenszel, Peto and inverse variance) and one random-effects method (DerSimonian and Laird inverse variance).

How to do a meta-analysis example? ›

2 Eight steps in conducting a meta-analysis
  1. 2.1 Step 1: defining the research question. ...
  2. 2.2 Step 2: literature search. ...
  3. 2.3 Step 3: choice of the effect size measure. ...
  4. 2.4 Step 4: choice of the analytical method used. ...
  5. 2.5 Step 5: choice of software. ...
  6. 2.6 Step 6: coding of effect sizes. ...
  7. 2.7 Step 7: analysis.
Nov 30, 2021

What do meta-analysis results indicate? ›

The results of each study included in the meta-analysis represent a study-specific effect size that varies around a mean population effect size. In other words, the results of each study in the meta-analysis are assumed to represent a unique effect.

What can meta genetics be used to determine? ›

Metagenomic research allows us to identify microorganisms, viruses, or free DNA that exist in the natural environment by identifying genes or DNA sequences from the organisms.

What kind of diseases are studied using genome wide association? ›

The genome-wide association can successfully identify the SNPs in the diseased individual. Diseases such as Parkinson's disease, Alzheimer's disease, and Crohn's disease have been studied by genome-wide association studies. These diseases are caused by a defect in multiple genes.

What are genome wide analysis methods? ›

Genome-wide association studies (GWAS) help scientists identify genes associated with a particular disease (or another trait). This method studies the entire set of DNA (the genome) of a large group of people, searching for small variations, called single nucleotide polymorphisms or SNPs (pronounced “snips”).

What are the criticisms of genome-wide association studies? ›

Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease.

What is meta gene analysis? ›

A metagene analysis is an average of quantitative data over one or more genomic regions (often genes or transcripts) aligned at some internal feature. For example, a metagene profile could be built around: the average of ribosome density surrounding the start codons of all transcripts in a ribosome profiling dataset.

What are genome-wide analysis methods? ›

Genome-wide association studies (GWAS) help scientists identify genes associated with a particular disease (or another trait). This method studies the entire set of DNA (the genome) of a large group of people, searching for small variations, called single nucleotide polymorphisms or SNPs (pronounced “snips”).

What is the metagenomic data analysis method? ›

The metagenomes are analyzed by comparing them with sequences already present in the databases or by a particular activity. For example, the software DOTUR is developed to study the operational taxonomic units, thus predicting the richness of the microbial population present in the given environment [42].

What is whole genome-wide analysis? ›

In genomics, a genome-wide association study (GWA study, or GWAS), is an observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait.

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