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Pathway analysis software tools | Genome-wide association study

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
Offers a suite of tools to assist in the analysis of high-throughput genomics data sets. GenGen provides to users the possibility to, among others, perform simple transmission disequilibrium test (TDT) association tests, case-control analysis, quantitative trait regression analysis, pathway-based association tests, allele coding discordance, linkage disequilibrium- (LD) based single-nucleotide polymorphism (SNP) selection or a genomic scanning.
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GeNets / Broad Institute Web Platform for Genome Networks
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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VEGAS / Versatile Gene-based Association Study
Aggregates association strength of individual markers into pre-specified biological pathways. VEGAS2 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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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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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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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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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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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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