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Pathway analysis software tools | Genome-wide association study

Genome-wide association (GWA) studies have typically focused on the analysis of single markers, which often lacks the power to uncover the relatively small effect sizes conferred by most genetic variants. Recently, pathway-based approaches have been developed, which use prior biological knowledge on gene function to facilitate more powerful analysis of GWA study data sets. These approaches typically examine whether a group of related genes in the same functional pathway are jointly associated with a trait of interest. Source text: Wang et al., 2010.

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PLINK
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A free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. The focus of PLINK is purely on analysis of genotype/phenotype data, so there is no support for steps prior to this (e.g. study design and planning, generating genotype or CNV calls from raw data). Through integration with gPLINK and Haploview, there is some support for the subsequent visualization, annotation and storage of results.
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GenGen
A suite of free software tools to facilitate the analysis of high-throughput genomics data sets. The package is currently a work-in-progress and infrequently updated.
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GeNets / Broad Institute Web Platform for Genome Networks
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Enables users to compare the global and local biological signal of networks. GeNets takes into consideration signal-to-noise ratio, coverage, density, and unique topology. It allows users to visualize, store, manage, and share pathway analyzes. Moreover, this tool intends to permit users to test any network they choose by training network-specific Quack models.
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i-GSEA4GWAS / improved GSEA for GWAS
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A web-based resource for analysis of GWAS data to identify pathways/gene sets correlated to certain traits by implementing an improved Gene Set Enrichment Analysis (i-GSEA) approach. i-GSEA4GWAS aims to establish an open platform to help further interpret the GWAS data to provide new insights in complex disease study, especially in complementation to the standard single variant/gene based analysis.
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MapMan
A user-driven tool that displays large datasets (e.g. gene expression data from Arabidopsis Affymetrix arrays) onto diagrams of metabolic pathways or other processes. MapMan was developed for use with Arabidopsis, but has already been extended for use with several other species. These tools are available as downloadable and web-based versions.
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PARIS / Pathway Analysis by Randomization Incorporating Structure
Determines aggregated association signals generated from genome-wide association study results. Pathway-based analyses highlight biological pathways associated with phenotypes. PARIS uses a unique permutation strategy to evaluate the genomic structure of interrogated pathways, through permutation testing of genomic features, thus eliminating many of the over-testing concerns arising with other pathway analysis approaches. We have updated PARIS to incorporate expanded pathway definitions through the incorporation of new expert knowledge from multiple database sources, through customized user provided pathways, and other improvements in user flexibility and functionality.
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GSA-SNP
Compiles several gene set analysis (GSA) approaches for genome wide association studies (GWAS). GSA-SNP gives access to three common-used methods: (i) the Z-statistic method; (ii) the restandardized GSA method; (iii) and GSEA. This program allows users to process single nucleotide polymorphisms, gene or haplotype data through both a graphic or command-line interface. These methods can be used for case-control or quantitative trait studies.
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ICSNPathway / Identify candidate Causal SNPs and Pathways
Identifies candidate causal single nucleotide polymorphisms (SNPs) and their corresponding candidate causal pathways from genome-wide association study (GWAS). ICSNPathway is a web server that integrates linkage disequilibrium (LD) analysis, functional SNP annotation and pathway-based analysis (PBA). The software can contribute to improve GWAS data interpretation from variants to biological mechanisms to better guide future biological mechanism studies.
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NetDecoder
A network biology-based computational platform designed to integrate transcriptomes, interactomes and gene ontologies to identify phenotype-specific subnetworks. NetDecoder is based on network flow algorithm and formulated as a minimum-cost flow optimization problem to identify and prioritize paths and key regulators within disease specific subnetworks. NetDecoder is designed to capture molecular switches and infer disease-specific networks to better understand pathways and identify key regulators that contribute to a disease phenotype.
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VEGAS / Versatile Gene-based Association Study
A free program for performing gene-based tests for association using the results from genetic association studies. After reading in SNP association p-values, it will annotate SNPs according to their position in genes, produce a gene-based test statistic, and then use simulation to calculate an empirical gene-based p-value.
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INRICH / INterval enRICHment analysis
Assists users for genome wide association studies. INRICH is a gene-set enrichment analysis tool that was developed for detecting enriched association signals of linkage disequilibrium (LD) independent genomic regions within biologically relevant gene sets. This method takes a set of independent, nominally associated genomic intervals and then tests for the enrichment of predefined gene-sets.
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GSEA-SNP
Besides improving power for association mapping, it may facilitate the identification of disease-associated SNPs and pathways, as well as the understanding of the underlying biological mechanisms. GSEA-SNP may also help to identify markers with weak effects, undetectable in association studies without pathway consideration.
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SNP ratio test
Assesses the enrichment of significant associations from genome-wide association studies (GWAS) in a pathway context. The SNP ratio test (SRT) compares the proportion of significant to all SNPs within genes that are part of a pathway and computes an empirical P-value based on comparisons to ratios in datasets where the assignment of case/control status has been randomized.
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Pathway Analysis using Random Forests
Provides a method for pathways identification. Pathway Analysis is an R package using either Random Forest classification or regression to extract: (i) informative pathways for permitting the classification of a categorical outcome as well as determining a continuous measure or; (ii) genes, to allow ranking between different groups or investigating variations in a targeted outcome. The application can be applied on microarray datasets related to various diseases and pathologies.
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PARADIGM-SHIFT
Predicts the impact of a mutation in a tumor sample using algorithm for each mutated gene. PARADIGM-SHIFT provides a prediction for each gene and each sample in the cohort and thereby brings a sample- or patient-specific assessment of the functional impact of a mutation. It also detects a difference in the expected activity of a gene in its downstream neighborhood relative to what is expected given its upstream neighborhood. PARADIGM-SHIFT makes use of two key pieces of information: (i) the known genetic interactions of a gene and (ii) the activation or deactivation of these interacting genes to gauge the impact of a mutational event.
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SNPsea
Identifies the conditions relevant to a trait by assessing the genes within associated loci for enrichment of condition specificity. SNPsea is a fast, robust and general C++ implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm. For a given set of SNPs, this general algorithm tests genes implicated by linkage disequilibrium (LD), in aggregate, for enrichment of specificity to a condition in a given matrix of genes and conditions.
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aSPU / adaptive Sum of Powered Score Test
We extend two adaptive tests for gene- and pathway-level association with a univariate trait to the case with GWAS summary statistics without individual-level genotype and phenotype data. We use the WTCCC GWAS data to evaluate and compare the proposed methods and several existing methods. We further illustrate their applications to a meta-analyzed dataset to identify genes and pathways associated with blood pressure, demonstrating the potential usefulness of the proposed methods. The methods are implemented in R package aSPU, freely and publicly available.
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PINBPA / Protein Interaction Network-Based Pathway Analysis
A tool to analyze GWAS data in a network fashion. User can easily import GWAS data, draw Manhattan plots, define blocks, prioritize genes with random walk with restart, detect enriched sub-networks and test the significance of sub-networks via a user-friendly interface.
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Path
Correlates users’ data with biological information from nine bioinformatics resources (Seattle SNPs, PharmGKB, IIDB, NCBI, OMIM, Genetic Association Database, dbSNP, KEGG, and UCSC Genome Browser). Path compares input data with information retrieved from the resources and conducts studies on the single nucleotide polymorphism (SNP)–SNP interactions according to user’s choices. Then, the imported data and results of the analysis are stored in a local database.
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ancGWAS
An algebraic graph-based centrality measure that accounts for linkage disequilibrium in identifying significant disease sub-networks by integrating the association signal from GWAS data sets into the human protein-protein interaction (PPI) network. We validated ancGWAS using an association study result from a breast cancer data set and the simulation of interactive disease loci in the simulation of a complex admixed population, as well as pathway-based GWAS simulation. This new approach holds promise for deconvoluting the interactions between genes underlying the pathogenesis of complex diseases.
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metaModules
Facilitates the automated identification of metabolic hotspots. metaModules provides a methodology for comparative metatranscriptomics. It can be used for datasets from any type of comparative metatranscriptomic studies. This method focuses on finding connected subsets of significantly deregulated ortholog gene groups. It uses statistics that are tailored to the properties of RNA-Seq data.
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traseR / TRait-Associated SNP EnRichment analysis
An easy-to-use R Bioconductor package that performs enrichment analyses of trait-associated SNPs in arbitrary genomic intervals with flexible options, including testing method, type of background and inclusion of SNPs in LD. traseR provides multiple options, including testing method, type of background and inclusion of SNPs in linkage disequilibrium (LD), to conduct statistical tests of taSNP enrichment for a given set of query genomic intervals.
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SigMod
Integrates genome-wide association studies (GWAS) results and gene network to identify a strongly interconnected gene module enriched in high association signals. SigMod is formulated as a binary quadratic optimization problem, which can be solved exactly through min-cut algorithms. Compared to existing methods, SigMod has several desirable properties: (i) edge weights quantifying confidence of connections between genes are taken into account, (ii) the selection path can be computed rapidly, (iii) the identified gene module is strongly interconnected, hence includes genes of high functional relevance, and (iv) the method is robust against noise from either the GWAS results or the network resource.
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PAPA
A flexible tool for pleiotropic pathway analysis utilizing GWAS summary results. The performance of PAPA was validated using publicly available GWAS summaries of body mass index and waist-hip ratio of the GIANT datasets. PAPA identified a set of pleiotropic pathways, which have been demonstrated to be involved in the development of obesity.
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NETAM / NETwork-driven Association Mapping
Detects ‘path associations’ from SNPs to phenotypes through gene expression traits. NETAM first constructs an association network, where nodes represent SNPs, gene traits or phenotypes, and edges represent the strength of association between two nodes. NETAM assigns a score to each path from an SNP to a phenotype, and then identifies significant paths based on the scores. In our simulation study, we show that NETAM finds significantly more phenotype-associated SNPs than traditional genotype–phenotype association analysis under false positive control, taking advantage of gene expression data.
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MAGENTA / Meta-Analysis Gene-set Enrichment of variaNT Associations
A computational tool that tests for enrichment of genetic associations in predefined biological processes or sets of functionally related genes, using genome-wide genetic data as input. MAGENTA is designed to analyze datasets for which genotype data are not readily available, such as large genome-wide association study (GWAS) meta-analyses. It can be used either (i) to test a specific hypothesis or (ii) to generate hypotheses by testing a range of known biological gene sets.
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MGA / MetaGeneAnnotator
A web app and a package for gene prediction which precisely predicts all kinds of prokaryotic genes from a single or a set of anonymous genomic sequences having a variety of lengths. MetaGeneAnnotator integrates statistical models of prophage genes, in addition to those of bacterial and archaeal genes, and also uses a self-training model from input sequences for predictions. The MGA can precisely predict genes even on short genomic sequences. Both typical and atypical genes can be sensitively and precisely detected while keeping high specificity.
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MITHrIL / Mirna enrIched paTHway Impact anaLysis
An algorithm developed for the analysis of signaling pathways. MITHrIL augments pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their degree of deregulation, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA.
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IndividPath
Finds deregulated pathways for individual disease samples. IndividPath compares the relative expression orderings (REOs) of intra-pathway gene pairs between each disease sample and the accumulated normal samples to proceed. It can be used to discover heterogeneous deregulation of multiple pathways. This tool was applied to determine overall survival of resected early-stage lung adenocarcinoma patients.
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MAGMA / Multi-marker Analysis of GenoMic Annotation
A fast and flexible tool for gene and gene-set analysis of GWAS genotype data. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties.
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Pascal / Pathway scoring algorithm
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Assists users in scoring gene and pathway. Pascal combines analytical and numerical solutions to correct for multiple testing on correlated data. It also integrates individual gene scores without the need for a threshold parameter to dichotomize gene scores for binary membership enrichment analysis. It can also assist with pathway enrichment.
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HNEP / Heterogeneous Network Edge Prediction
Produces biologically-meaningful predictions by integrating multiple high-throughput data sources. The approach computes features describing the network topology connecting two nodes. These features are used as input to a machine learning method which predicts the probability that an edge exists. Through evaluating the informativeness of each feature, the relevance of included domains can be compared providing insight into the influential mechanisms behind the process of interest. HNEP effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains.
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snpGeneSets
A uniform-score gene-set analysis (USGSA) to unify different gene measures by a uniform score for identifying pathways from GWAS data, and use a pre-generated permutation distribution table to quickly obtain multiple-testing adjusted p-value. It is a computationally efficient approach for pathway analysis of GWAS data with promoted interpretability and comparability. USGSA is implemented in snpGeneSets.
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LENS / Lens for Enrichment and Network Studies of human proteins
Computes a large number and enrichment statistics with a single click. LENS can perform network-based analyses and aims to assist researchers to make inferences of the potential significance of their results. It can suggest possible auto-complete options, from which users select an item of interest. This tool reports values that describe the network connectivity: minimum shortest path length, average shortest path length, and the count of disconnected nodes.
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Postgwas
A comprehensive toolkit for post-processing, visualization and advanced analysis of GWAS results. Postgwas supports virtually all model organisms and represents the first cohesive implementation of such tools for the popular language R.
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PUPPI / Pathway analysis Using Protein-Protein Interaction networks
A pathway association test for genome-wide association studies (GWAS) with case-control samples. PUPPI aggregates the interaction signals in protein-protein interaction (PPI) networks within a pathway. The test was applied to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. PUPPI can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. The power simulation results suggested that the PUPPI can have higher or comparable power to that of PLINK, HYST, and SKAT in some models when there were both main effects and interaction effects.
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Pointer
Provides a useful framework for the integrative analysis of pharmacogenetic GWAS data, by increasing the power to detect aggregate effects of multiple low risk variants. A key advantage of Pointer is the capability to associate SNPs with genes by exploiting physical proximity as well as by using expression quantitative trait loci (eQTLs) that capture information about both cis- and trans-acting regulatory effects.
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Pathway-PDT
Uses the framework of pedigree disequilibrium test (PDT) for general family data, to perform pathway analysis based on raw genotypes in family-based GWAS. Pathway-PDT also can be more powerful than the PLINK set-based test when analyzing general nuclear families with multiple siblings or missing parents. Additionally, Pathway-PDT has a flexible and convenient user interface, which allows users to modify their analysis parameters as well as to apply various types of gene and pathway definitions.
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VEGAS2Pathway
Aggregates association strength of individual markers into pre-specified biological pathways. VEGAS2Pathway is a a versatile pathway-based approach for genome-wide association studies (GWAS) data that accounts for gene size and linkage disequilibrium between markers using simulations from the multivariate normal distribution. First, it calculates the gene-based test statistics for all genes using the VEGAS (VErsatile Gene-based Association Study) approach which accounts for the linkage disequilibrium (LD) between the single nucleotide polymorphisms (SNPs) within a gene through simulation. Second, for each of a set of pre-specified gene-sets, the relevant gene-based results are carried forward to compute a pathway-based test.
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PathCORE
Maps each gene in each feature to the one pathway in which it has the greatest estimated impact. PathCORE overlays curated knowledge after feature construction to help researchers interpret constructed features in the context of existing knowledgebase. PathCORE analyses the bacterium P. aeruginosa and human pan-cancer datasets, using two different feature construction methods (ensemble Analysis using Denoising Autoencoders for Gene Expression (eADAGE) and non-negative matrix factorization (NMF)).
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YousafEtAl2018
New
Aligns the genetics of neuropsychological traits to the molecular network of the human brain. This method makes uniform the process for data integration at genotype, phenotype and brain transcriptome level. It is useful for conducting a variant-based, genome-wide association investigation concentrated on neuropsychiatric disorders. This tool can include whole genome mRNA expression data of the human developing brain.
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BioCor
Calculates a functional similarity measure between gene identifiers based on the pathways described on KEGG and REACTOME. BioCor is an R package. It offers functions to calculate the similarity between two pathways or two genes, to calculate the similarity between two clusters of genes by joining pathways of each gene or by comparing the similarity between the genes of a cluster. It also allows to combine the value of matrices of similarities or to convert similarity measures.
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ExRAT
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Determines gene-gene interactions and disease related genes. ExRAT proposes a standalone application based on a rapid association test method intending to report disease association by investigating the possible interactions between disease related genes and single nucleotide polymorphisms (SNPs). The program aims to assist researchers in highlighting interactions linked with disease risk.
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MORPH bulk
Permits users to run genome-wide MORPH analyses. MORPH bulk can be used in two modes: automatic pipeline mode or step-by-step mode, allowing full control over the analysis pipeline. It includes installation instructions and a step-by-step protocol for analyses. Random runs can be deployed to perform permutation-test based significance assessment. In a random run, for each desired bait set size, n random sets of bait genes are picked from a randomly chosen data set.
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EW_dmGWAS
Obsolete
Searches for dense modules in a human protein-protein interaction (PPI) network; it has since become a popular tool for network-assisted analysis of genome-wide association studies (GWAS). dmGWAS weights nodes by using GWAS signals. EW_dmGWAS is an upgraded algorithm to boost GWAS signals in a node- and edge-weighted PPI network.
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PANOGA / Pathway and Network Oriented GWAS Analysis
Obsolete
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A web server for post-genome-wide associatio analysis. PANOGA supports pathway and network-oriented SNP–phenotype association analysis in a single system without requiring any additional manual processes. The strength of our methodology stems from its multidimensional perspective, where we combine evidence from the following five resources: (i) genetic association information obtained through GWAS, (ii) SNP functional information, (iii) protein–protein interaction network, (iv) linkage disequilibrium and (v) biochemical pathways. With its multifactorial basis, PANOGA has a good potential to decipher the combination of biological processes underlying disease.
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ALIGATOR / Association LIst Go AnnoTatOR
Obsolete
A program for testing for Gene Ontology categories over-represented on a list of significant SNPs from a GWA analysis. This method corrects for linkage disequilibrium between SNPs, variable gene size, and multiple testing of nonindependent pathways.
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GRASS / gene set ridge regression in association studies
Obsolete
Conducts regularized logistic regression and evaluates the gene set association. GRASS is designed to integrate covariates and includes gene-gene and gene-environment interaction effects at the single nucleotide polymorphism (SNP) level. It synthetizes the genetic variation in each gene using a gene-set-based analysis. This tool can recognize signals based on predefined gene sets via regularized regression.
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GWAS module
Obsolete
Identifies and integrates tissue-specific functional network with imaging genome wide association studies (GWAS) results. GWAS module introduces regression models in addition to classification models for re-prioritizing GWAS results with network information. It employs re-prioritized results to identify GWAS-enriched network modules. This tool incorporates GWAS data and tissue-specific network with machine learning models.
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