Package: rMVP 1.4.0

Xiaolei Liu

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>).

Authors:Lilin 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]

rMVP_1.4.0.tar.gz
rMVP_1.4.0.tar.gz(r-4.5-noble)rMVP_1.4.0.tar.gz(r-4.4-noble)
rMVP_1.4.0.tgz(r-4.4-emscripten)rMVP_1.4.0.tgz(r-4.3-emscripten)
rMVP.pdf |rMVP.html✨
rMVP/json (API)

# Install 'rMVP' in R:
install.packages('rMVP', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/xiaolei-lab/rmvp/issues44 issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • pig60K - Genotyped by pig 60k chip

On CRAN:

Conda:

openblascppopenmp

2.60 score 938 downloads 12 mentions 32 exports 10 dependencies

Last updated 2 months agofrom:2dfde6f970. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 23 2025
R-4.5-linux-x86_64OKMar 23 2025
R-4.4-linux-x86_64OKMar 23 2025

Exports:as.big.matrixattach.big.matrixdeepcopyis.big.matrixMVPMVP.BRENT.Vg.VeMVP.DataMVP.Data.Bfile2MVPMVP.Data.Hapmap2MVPMVP.Data.imputeMVP.Data.KinMVP.Data.MapMVP.Data.MVP2BfileMVP.Data.Numeric2MVPMVP.Data.PCMVP.Data.PhenoMVP.Data.VCF2MVPMVP.EMMA.Vg.VeMVP.FarmCPUMVP.FaSTLMM.LLMVP.GLMMVP.HE.Vg.VeMVP.HistMVP.K.VanRadenMVP.MLMMVP.PCAMVP.PCAplotMVP.ReportMVP.Report.DensityMVP.Report.QQplotMVP.Versionnew

Dependencies:BHbigmemorybigmemory.sriMASSRcppRcppArmadilloRcppEigenRcppProgressRhpcBLASctluuid

Citation

To cite package ‘rMVP’ in publications use:

Yin L, Zhang H, Tang Z, Xu J, Yin D, Zhang Z, Yuan X, Zhu M, Zhao S, Li X, Liu X (2025). rMVP: Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool. R package version 1.4.0, https://CRAN.R-project.org/package=rMVP.

Corresponding BibTeX entry: 

  @Manual{,
    title = {rMVP: Memory-Efficient, Visualize-Enhanced,
      Parallel-Accelerated GWAS Tool},
    author = {Lilin Yin and Haohao Zhang and Zhenshuang Tang and Jingya
      Xu and Dong Yin and Zhiwu Zhang and Xiaohui Yuan and Mengjin Zhu
      and Shuhong Zhao and Xinyun Li and Xiaolei Liu},
    year = {2025},
    note = {R package version 1.4.0},
    url = {https://CRAN.R-project.org/package=rMVP},
  }

Readme and manuals

Help Manual

Help pageTopics
MVP, Memory-efficient, Visualization-enhanced, Parallel-acceleratedMVP
MVP.BRENT.Vg.Ve variance component estimation using the BRENT methodMVP.BRENT.Vg.Ve
MVP.Data: To prepare data for MVP packageMVP.Data
MVP.Data.Bfile2MVP: To transform plink binary data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.Bfile2MVP
MVP.Data.Hapmap2MVP: To transform Hapmap data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.Hapmap2MVP
MVP.Data.impute: To impute the missing genotype Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.impute
KinshipMVP.Data.Kin
MVP.Data.Map: To check map file Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.Map
MVP.Data.MVP2Bfile: To transform MVP data to binary format Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.MVP2Bfile
MVP.Data.Numeric2MVP: To transform Numeric data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.Numeric2MVP
Principal component analysisMVP.Data.PC
MVP.Data.Pheno: To clean up phenotype file Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.Pheno
MVP.Data.VCF2MVP: To transform vcf data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018MVP.Data.VCF2MVP
Estimate variance components using EMMAMVP.EMMA.Vg.Ve
Perform GWAS using FarmCPU methodMVP.FarmCPU
Evaluation of the maximum likelihood using FaST-LMM methodMVP.FaSTLMM.LL
To perform GWAS with GLM and MLM model and get the P value of SNPsMVP.GLM
To estimate variance component using HE regressionMVP.HE.Vg.Ve
Phenotype distribution histogramMVP.Hist
Calculate Kinship matrix by VanRaden methodMVP.K.VanRaden
To perform GWAS with GLM and MLM model and get the P value of SNPsMVP.MLM
Principal Component AnalysisMVP.PCA
PCA PlotMVP.PCAplot
MVP.ReportMVP.Report
SNP DensityMVP.Report.Density
QQ PlotMVP.Report.QQplot
Print MVP BannerMVP.Version
Genotyped by pig 60k chippig60K