rMVP - 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)
<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>).
