rMVP

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) doi:10.1038/ng1847), MLM (Jianming Yu (2006) doi:10.1038/ng1702) and FarmCPU (Xiaolei Liu (2016) doi:10.1371/journal.pgen.1005767); variance components estimation methods EMMAX (Hyunmin Kang (2008) doi:10.1534/genetics.107.080101;), FaSTLMM (method: Christoph Lippert (2011) doi:10.1038/nmeth.1681, R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) doi:10.1371/journal.pone.0107684 and 'SUPER': Qishan Wang and Feng Tian (2014) doi:10.1371/journal.pone.0107684), and HE regression (Xiang Zhou (2017) doi:10.1214/17-AOAS1052).