rMVP <img alt="Hits" src="https://hits.sh/github.com/xiaolei-lab/rMVP.svg?view=today-total&extraCount=81834"/>

An r package for Memory-efficient, Visualization-enhanced, and Parallel-accelerated Genome-Wide Association Study

Repos:

Github: https://github.com/xiaolei-lab/rMVP
Gitee(quick access in China): https://gitee.com/xiaolei-lab/rMVP

Authors:

Design and Maintenance: Lilin Yin#, Haohao Zhang#, Shuhong Zhao, Xinyun Li, and Xiaolei Liu.
Contributors: Zhenshuang Tang, Jingya Xu, Dong Yin, Zhiwu Zhang, Xiaohui Yuan, Mengjin Zhu Citation: Yin L, Zhang H, Tang Z, Xu J, Yin D, Zhang Z, Yuan X, Zhu M, Zhao S, Li X. rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated tool for Genome-Wide Association Study, Genomics, Proteomics & Bioinformatics , 2021, 19 (4), 619-628, doi: 10.1016/j.gpb.2020.10.007.

Questions, suggestions, and bug reports are welcome and appreciated: xiaoleiliu@mail.hzau.edu.cn

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Installation
Data Preparation
Data Input
- Basic
- Advanced
Start GWAS
Output
Citation
FAQ and Hints

Installation

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WE STRONGLY RECOMMEND TO link MKL or OpenBLAS with R to accelerate parallel computing. To install rMVP in R:

The stable version:

install.packages("rMVP")

The latest version:

devtools::install_github("xiaolei-lab/rMVP")

After installed successfully, rMVP can be loaded by typing

library(rMVP)

Typing ?rMVP could get the details of all parameters.

For more help on Windows installation, see the wiki page (Chinese)

Data Preparation

Phenotype

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We suggest to provide the phenotype file, because users needn't to manually pre-treat the order of phenotype and genotype individuals, rMVP could automatically adjust the order of phenotype file to be consistent with genotype file. Note that if the phenotype is provided in data conversion, rMVP will generate a new phenotype file, please remember to load it for analysis rather than original one.

| Taxa | trait1 | trait2 | trait3 | | :---: | :---: |:---: |:---: | |33-16|101.5|0.25|0| |38-11| 102.7|0.23|1| |4226 |101.2|-0.17|1| |4722| 105.5|NA|0| |A188 |108.1|0.57|1| |A214N| 95.13|0.87|0| |A239 |100.2|-0.16|1|

Genotype

PLINK binary

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If you have genotype data in PLINK Binary format (details see http://zzz.bwh.harvard.edu/plink/data.shtml#bed):

fileBed, name of genotype data in PLINK Binary format
fileKin, TRUE or FALSE, if TRUE, kinship matrix represents relationship among individuals will be calculated
filePC, TRUE or FALSE, if TRUE, principal component analysis will be performed
out, prefix of output file
maxLine, number, the number of markers read into memory

# Full-featured function (Recommended)
MVP.Data(fileBed="plink",
         filePhe=NULL,
         fileKin=FALSE,
         filePC=FALSE,       
         #maxLine=10000,
         out="mvp.plink"
         )
         
# Only convert genotypes
MVP.Data.Bfile2MVP(bfile="plink", out='mvp', maxLine=1e4) # the genotype data should be fully imputed before using this function

VCF

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If you have genotype data in VCF format:
fileVCF, name of genotype data in VCF format
filePhe, name of phenotype data
sep.phe, separator of phenotype file
fileKin, TRUE or FALSE, if TRUE, kinship matrix represents relationship among individuals will be calculated
filePC, TRUE or FALSE, if TRUE, principal component analysis will be performed
out, the prefix of output file

##fileformat=VCFv4.2
##fileDate=20171105
##source=PLINKv1.90
##contig=<ID=1,length=2>
##INFO=<ID=PR,Number=0,Type=Flag,Description="Provisional reference allele, may not be based on real reference genome">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	-9_CZTB0004	-9_CZTB0006	-9_CZTB0008	-9_CZTB0010	-9_CZTB0011	-9_CZTB0012
1	1	10000235	A	C	.	.	PR	GT	0/1	0/0	0/0	0/0	0/0	0/1
1	1	10000345	A	G	.	.	PR	GT	0/0	0/0	0/0	0/0	1/1	1/1
1	1	10004575	G	.	.	.	PR	GT	0/0	0/0	0/0	0/0	0/0	0/0
1	1	10006974	C	T	.	.	PR	GT	0/0	0/0	0/1	1/1	0/1	1/1
1	1	10006986	A	G	.	.	PR	GT	0/0	0/0	0/1	./.	1/1	1/1

# Full-featured function (Recommended)
MVP.Data(fileVCF="myVCF.vcf",
         #filePhe="Phenotype.txt",
         fileKin=FALSE,
         filePC=FALSE,
         out="mvp.vcf"
         )

# Only convert genotypes
MVP.Data.VCF2MVP("myVCF.vcf", out='mvp') # the genotype data should be fully imputed before using this function

Hapmap

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If you have genotype data in Hapmap format:

fileHMP, a string or a string vector, e.g. fileHMP = "hapmap.txt" or fileHMP = c("chr1.hmp.txt", "chr2.hmp.txt", "chr3.hmp.txt")
filePhe, name of phenotype file
sep.phe, separator of phenotype file
fileKin, TRUE or FALSE, if TRUE, kinship matrix represents relationship among individuals will be calculated
filePC, TRUE or FALSE, if TRUE, principal component analysis will be performed
out, the prefix of output file
maxLine, number, the number of markers read into memory

hapmap.txt

| rs# | alleles | chrom | pos | strand | assembly# | center | protLSID | assayLSID | panelLSID | QCcode | 33-16 | 38-11 | 4226 | 4722 | A188 | ... | A239 | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | rs3683945 | G/A | 1 | 3197400 | + | NA

RMVP

Install / Use

README