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>).

Getting started

Package details

AuthorLilin 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]
MaintainerXiaolei Liu <xiaoleiliu@mail.hzau.edu.cn>
LicenseApache License 2.0
Version1.0.6
URL https://github.com/xiaolei-lab/rMVP
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("rMVP")

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rMVP documentation built on April 18, 2021, 5:07 p.m.