Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS
Tool
Description
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) ).