Pathway analysis software tools | Genome-wide association study

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.

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.

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.

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.

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.