The package integrates local genomic annotation databases based on NCBI dbSNP 138 and 142, Entrez Gene 105 and 106 and MSigDB V4.0, and provides genome-wide annotation for SNP, Gene and gene sets. It aims to support interpretation of genome-wide study (GWS) results and performing post-analysis. The package implements three categories of functions: 1) genomic mapping annotation for SNPs and genes, and function annotation for gene sets; 2) bidirectional mapping relationship between SNPs and genes, and between genes and gene sets; and 3) flexible gene effect measure by SNP association and function-related gene set/pathway identification by enrichment analysis. The auxiliary functions are also provided to facilitate annotation and analysis.
Reference: Mei H, Li L, Jiang F, Simino J, Griswold M, Mosley T, Liu S. snpGeneSets: An R Package for Genome-Wide Study Annotation. G3 2016, 6(12):4087-4095. PMID:27807048
Reference: Mei H, Li L, Liu S, Jiang F, Griswold M, Mosley T. The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits. BMC genomics 2015, 16(1):336. PMID:25898945
The software is written in C++ and it aims to detect gene-gene interaction in the pedigree and population data.
Both source codes and compiled program can be downloaded here
Reference: Mei H, Cuccaro ML, Martin ER. Multifactor dimensionality reduction-phenomics: a novel method to capture genetic heterogeneity with use of phenotypic variables. Am J Hum Genet. 2007 Dec; 81(6):1251-61
The software is written in C++ and it aims to detect gene-gene interaction for population-based case control study. The software implemented different cross-validation and statistics for fast computing permutation p-value of multi-loci interactions for balanced or un-balanced case-control data.
Program (UNIX, Linux and Windows): EMDR1.83.zip
C++ Source Code: EMDR1.83_C++.zip
Reference: Mei, H., Ma, D., Ashley-Koch, A., and Martin, E.R. (2005). Extension of multifactor dimensionality reduction for identifying multilocus effects in the GAW14 simulated data. BMC genetics 6 Suppl 1, S145.