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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.
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.
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.
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.
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.
INRICH / INterval enRICHment analysis
A pathway-based genome-wide association analysis tool that tests for enriched association signals of predefined gene-sets across independent genomic intervals. INRICH has wide applicability, fast running time and, most importantly, robustness to potential genomic biases and confounding factors. INRICH can 1) conduct pathway analysis on any type of genomic variation data, including but not limited to SNPs, CNVs, genes, as well as their combination; 2) run the positional clustering test of genomic intervals, which can be used to detect genomic regions with multiple independent, non-randomly clustered risk variants; 3) perform pathway analysis on multiple lists of associated genomic regions, which can be used for detecting functional gene sets with pleiotropic effects across related disorders or for a comparative study of pathway analysis across different datasets.
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.
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.
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.
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.
A methodology for comparative metatranscriptomics that facilitates the automated identification of metabolic hotspots that are significantly deregulated in disease, and recovered disease-associated functions in two oral microbiome-mediated disorders. Using metaModules, we recovered significantly deregulated subnetworks that were indeed recognized to be involved in two well-studied, microbiome-mediated oral diseases, such as butanoate production in periodontal disease and metabolism of sugar alcohols in dental caries. More importantly, our results indicate that metaModules can be used for hypothesis generation based on automated discovery of novel, disease-related functional subnetworks, which would otherwise require extensive and laborious manual assessment.
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.
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.
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.
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.
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.
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.
Pascal / Pathway scoring algorithm
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An easy-to-use tool for gene scoring and pathway analysis from GWAS results. Pascal uses external data to estimate linkage disequilibrium. Therefore, the user only needs to supply genome wide SNP p-values. Pascal then derives p-values for genes and predefined pathways. Pascal doesn’t use Monte-Carlo simulation to derive gene p-values. This leads to increased speed and accuracy. This speed in the gene scoring is then leveraged to control the false positive rate in pathway scoring. For pathway scoring, we implemented and tested enrichment strategies that compared very favorably compared to hypergeometric enrichment. This comparison was done on a large collection of GWAS results giving us confidence to recommend Pascal for downstream analysis of GWAS results.
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.
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.
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.
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.
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.
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)).
PANOGA / Pathway and Network Oriented GWAS Analysis
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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.
SIFORM / Shared Informative FactOR Models
A statistical framework to detect predictive biomarkers for a response variable of interest, which is typically a disease-associated phenotype. SIFORM explores associations between a disease phenotype and high-throughput genetic data generated from multiple platforms. It also produces direct rankings of genetic variables in terms of the strength of association with the response variable. SIFORM incorporates the disease phenotype information in the factor space to detect genetic variables that interact with the response variable. SIFORM detects the most true biomarkers with the smallest FDR among all the others evaluated methods. The proposed framework appears to fit the lung cancer data reasonably well.
NIMMI / Network Interface Miner for Multigenic Interactions
Helps investigators to prioritize genes and networks related to a particular phenotype after a Genome-wide association study (GWAS). NIMMI is an open-source tool that takes into account information on biological relationships to help the interpretation of GWAS data and to prioritize trait networks for further study. It offers several advantages over other network and pathway-based approaches. This method can also identify important genes involved in a multi-genic trait with a high degree of consistency and reproducibility, even across datasets of differing size and ancestry.
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