Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool
MVP.BRENT.Vg.Ve variance component estimation using the BRENT method
MVP.Data.Bfile2MVP: To transform plink binary data to MVP package Auth...
MVP.Data.Hapmap2MVP: To transform Hapmap data to MVP package Author: H...
MVP.Data.impute: To impute the missing genotype Author: Haohao Zhang B...
Kinship
MVP.Data.Map: To check map file Author: Haohao Zhang Build date: Sep 1...
MVP.Data.MVP2Bfile: To transform MVP data to binary format Author: Hao...
MVP.Data.Numeric2MVP: To transform Numeric data to MVP package Author:...
Principal component analysis
MVP.Data.Pheno: To clean up phenotype file Author: Haohao Zhang Build ...
MVP.Data: To prepare data for MVP package Author: Xiaolei Liu, Lilin Y...
MVP.Data.VCF2MVP: To transform vcf data to MVP package Author: Haohao ...
Estimate variance components using EMMA
Perform GWAS using FarmCPU method
Evaluation of the maximum likelihood using FaST-LMM method
To perform GWAS with GLM and MLM model and get the P value of SNPs
To estimate variance component using HE regression
Phenotype distribution histogram
Calculate Kinship matrix by VanRaden method
To perform GWAS with GLM and MLM model and get the P value of SNPs
Principal Component Analysis
PCA Plot
MVP, Memory-efficient, Visualization-enhanced, Parallel-accelerated
SNP Density
QQ Plot
MVP.Report
Print MVP Banner
Objects exported from other packages
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>).