Meta-analysis software tools | Genome-wide association study
Over the last decade, genome-wide association studies (GWAS) have become the standard tool for gene discovery in human disease research. While debate continues about how to get the most out of these studies and on occasion about how much value these studies really provide, it is clear that many of the strongest results have come from large-scale mega-consortia and/or meta-analyses that combine data from up to dozens of studies and tens of thousands of subjects.
Performs meta-analysis of genome-wide association studies (GWASs). META is designed to summarize the evidence from different association studies. It can work seamlessly with the output of SNPTEST. This tool converts study-specific P-values and direction of effect into a signed Z-statistic. It allows for incompatibility between phenotype units.
A software tool to look for similarities between 700 traits, build trees with informative clusters, and highlight underlying pathways. Clusters are consistent with pre-defined groups and literature-based validation but also reveal novel connections. CPAG will become increasingly powerful as more genetic variants are uncovered, leading to a deeper understanding of complex traits.
Provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats.
Provides functions for conducting meta-analyses in R. The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. The package provides various plot functions (for example, for forest, funnel, and radial plots) and functions for assessing the model fit, for obtaining case diagnostics, and for tests of publication bias.
A general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. MetaSKAT can carry out meta-analysis of SKAT, SKAT-O and burden tests with individual level genotype data or gene level summary statistics.
Offers a set of methods for the meta-analysis of genome-wide single nucleotides polymorphisms (SNP) association results. MetABEL is a package composed of three main functions: (i) one dedicated to the generation of forest plots; (ii) one which is able to perform a meta-analysis of results derived from several genome wide associations studies (GWAS); (iii) and one that offers a feature for the pairwise meta-analysis of results from GWAS.
Allows meta-analysis of rare variant association studies for quantitative traits. RAREMETAL works in two steps: (i) analysis of individual studies and (ii) generation of summary statistics that can later be combined across studies. The software enables users to customize variant groupings for gene-level statistics at the meta-analysis stage, after individual studies are analyzed. It can be used in large meta-analyses of rare variants for a variety of traits, ranging from blood lipids levels, anthropometric traits to smoking and drinking.
A computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.
A command-line program to perform meta-analysis of sequencing studies by combining the score statistics from multiple studies. MASS implements three types of multivariate tests that encompass all commonly used association tests for rare variants. The input files can be generated from the accompanying software SCORE-Seq. This bundle of programs allows analysis of large sequencing studies in a time and memory efficient manner.
As new methods for multivariate analysis of Genome Wide Association Studies (GWAS) become available, it is important to be able to combine results from different cohorts in a meta-analysis. The R package MultiMeta provides an implementation of the inverse-variance based method for meta-analysis, generalized to an n-dimensional setting.
Allows effective dimension reduction by combining multiple transcriptomic (or epigenetic) studies. MetaPCA is a meta-analytic principal component analysis (PCA) framework that combines multiple transcriptomic or epigenomic datasets to identify a common eigen-space for dimension reduction. The software was applied to three transcriptomic examples from yeast cell cycle, prostate cancer, mouse metabolism and methylation data from TCGA pan-cancer studies.
Implements robust methods for analysis and meta-analysis of genome-wide association studies (GWAS) within the statistical package Stata. GWAR proposes the Cochran-Armitage trend test under a recessive, additive and dominant model of inheritance as well as robust methods based on the MERT statistic, the MAX statistic and the MIN2. All the aforementioned approaches were employed in a fixed or a random effects meta-analysis setting for summary data with weights equal to the reciprocal of the combined cases and controls.
Allows the joint analysis of individual-level data and summary statistics. LEP is a package providing a statistical approach able to fit model with the aim of exploiting genome wide analysis studies (GWAS) data and it assists users in characterizing the pleiotropy. This program intends to improve the identification of risk variants and the risk prediction.
Provides an approach for the increasing of meta-analysis that overlaps samples. FOLD is a standalone software, based on summary-statistic-based method, which intends to classify subjects according to their contributions to the final statistic and, then, determine the summary statistic for each category. The application can be used in conjunction with the FOLD-split software if users have to determine a splitting design.
Assists users in association mapping of pair-wise traits. iMAP is a mixture modeling method that relies on a multinomial logistic regression model to incorporate a large number of binary and continuous single-nucleotide polymorphism (SNP) annotations. This application directly models summary statistics from genome-wide association studies (GWASs) and uses a multivariate Gaussian distribution to account for phenotypic correlation between traits.
Infers the statistical power to detect associated single nucleotide polymorphisms (SNPs) and the predictive accuracy of the poly-genic scores (PGS) in a meta-analysis of genome-wide association studies (GWAS) results from genetically and phenotypically heterogeneous studies. The MetaGAP calculator assumes the use of a fixed-effects meta-analysis method. The MetaGAP calculator helps researchers to gauge how sensitive their results will be to heterogeneity in genetic effects across studies
Measures the evidence of aggregate-level pleiotropic association and estimates an optimal subset of traits associated with the risk locus. CPBayes uses a unified Bayesian statistical framework based on a spike and slab prior. It performs a fully Bayesian analysis by employing the Markov chain Monte Carlo (MCMC) technique Gibbs sampling. The tool analyzes pleiotropy using summary-level data across a wide range of studies for two or more phenotypes - separate genome-wide association studies (GWAS) with or without shared subjects, cohort study for multiple traits.
Performs gene-based cross-phenotype association analysis. QRFCCA aims to reduces the dimensions of both genotype and phenotype data while fully retaining the original genotypic and phenotypic information. It can be used for association analysis of both common variants and rare variants, and any phenotypes including quantitative or qualitative, multivariate or function-valued phenotypes.
Facilitates the exchange of information between software packages for meta-analysis of rare-variant associations. PreMeta reformats the output files of study-level summary statistics generated by MASS, RAREMETAL, MetaSKAT and seqMeta. It can transform summary to make it correspond with other package in order to perform meta-analysis. The tool can check allele mismatches and correct summary statistics.
A comprehensive tool with both powerful command-line and user-friendly web interface versions to predict, and convert both genome builds and allele definitions between multiple GWAS (or deep sequencing) genotype data, which is required for all imputations and genome-wide meta-analyses. GACT will facilitate and ease a broad use of the GWAS data from the dbGaP and other publicly available genotype repositories for large-scale secondary analyses and multi-laboratory collaborations in the genetic association studies of human diseases.