MVP.Version

Build date: Aug 30, 2017
Last update: Dec 12, 2018

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>).

Xiaolei Liu

rMVP

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

Lilin Yin

Haohao Zhang

Zhenshuang Tang

Jingya Xu

Dong Yin

Zhiwu Zhang

Xiaohui Yuan

Mengjin Zhu

Shuhong Zhao

Xinyun Li

Qishan Wang

Feng Tian

Hyunmin Kang

Xiang Zhou

MVP.Version function

<dl><dt>width</dt>
<dd>the width of the message</dd>
<dt>verbose</dt>
<dd>whether to print detail.</dd></dl>

Arguments

Lilin Yin, Haohao Zhang, and Xiaolei Liu

Author

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<dl>

<dt>width</dt>
<dd>the width of the message</dd>


<dt>verbose</dt>
<dd>whether to print detail.</dd>

</dl>

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Description

Usage

Value

Arguments

Author

Examples