SNPrank is an eigenvector centrality algorithm that ranks the importance of single nucleotide polymorphisms (SNPs) in a genetic association interaction network (GAIN). Each SNP is ranked according to its overall contribution to the phenotype, including its main effect and second- and higher-order gene-gene interactions.
power GWAS interaction is the code for approximate power calculations for identification of gene x gene and gene x environment interactions in genomewide association studies using a two-stage analysis.
LocusTrack is a web-based application that annotates and creates plots of regional GWAS results and incorporates user-specified tracks that display annotations such as linkage disequilibrium (LD), phylogenetic conservation, chromatin state, and other genomic and regulatory elements.
gdsfmt and SNPRelate are high-performance computing R packages for multi-core symmetric multiprocessing computer architectures. They are used to accelerate two key computations is GWAS: principal component analysis (PCA) and relatedness analysis using identity-by-descent (IBD) measures. The kernels of our algorithms are written in C/C++, and have been highly optimized. Benchmarks show the uniprocessor implementations of PCA and IBD are ~8 to 50 times faster than the implementations provided by the popular EIGENSTRAT (v3.0) and PLINK (v1.07) programs respectively, and can be sped up to 30~300 folds by utilizing eight cores. SNPRelate can analyze tens of thousands of samples, with millions of SNPs.
CPAG can estimate disease and trait similarity, identify informative disease clusters, and carry out pathway enrichment analysis. It also provides visualization of these results in the form of hierarchical clustering trees, heatmaps, and networks.
PINBPA (Protein interaction network-based pathway analysis) for genome-wide association studies (GWAS) has been developed as a Cytoscape app, to enable analysis of GWAS data in a network fashion.
