Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool

A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. 'rMVP' contains three models GLM (Alkes Price (2006) ), MLM (Jianming Yu (2006) ) and FarmCPU (Xiaolei Liu (2016) ); variance components estimation methods EMMAX (Hyunmin Kang (2008) ;), FaSTLMM (method: Christoph Lippert (2011) , R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) and 'SUPER': Qishan Wang and Feng Tian (2014) ), and HE regression (Xiang Zhou (2017) ).


Reference manual

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1.0.6 by Xiaolei Liu, 6 months ago

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Authors: Lilin Yin [aut] , Haohao Zhang [aut] , Zhenshuang Tang [aut] , Jingya Xu [aut] , Dong Yin [aut] , Zhiwu Zhang [aut] , Xiaohui Yuan [aut] , Mengjin Zhu [aut] , Shuhong Zhao [aut] , Xinyun Li [aut] , Qishan Wang [ctb] , Feng Tian [ctb] , Hyunmin Kang [ctb] , Xiang Zhou [ctb] , Xiaolei Liu [cre, aut, cph]

Documentation:   PDF Manual  

Apache License 2.0 license

Imports utils, stats, methods, graphics, grDevices, MASS, Rcpp, bigmemory

Suggests knitr, testthat, rmarkdown

Linking to Rcpp, RcppArmadillo, RcppEigen, RcppProgress, BH, bigmemory

System requirements: C++11

See at CRAN