XiaoleiLiuBio/MVP
Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool

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README.md
In XiaoleiLiuBio/MVP: Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool

rMVP CRAN Version

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

#----------------------rMVP v1.1.0 is coming, and stronger again!------------------------

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

:toolbox: Relevant software tools for genetic analyses and genomic breeding

📫 HIBLUP: Versatile and easy-to-use GS toolbox. 🍀 SIMER: data simulation for life science and breeding. 🚴‍♂️ KAML: Advanced GS method for complex traits. 🏔️ IAnimal: an omics knowledgebase for animals. 🏊 hibayes: A Bayesian-based GWAS and GS tool. 📊 CMplot: A drawing tool for genetic analyses.

Contents

Installation

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

install.packages("rMVP")

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

back to top 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

back to top 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

back to top 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

back to top 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 | NA | NA | NA | NA | NA | AG | AG | GG | AG | GG | ... | AA | | rs3707673 | A/G | 1 | 3407393 | + | NA | NA | NA | NA | NA | NA | GA | GA | AA | GA | AA | ... | GG | | rs6269442 | G/A | 1 | 3492195 | + | NA | NA | NA | NA | NA | NA | AG | GG | GG | AG | GG | ... | AA | | rs6336442 | G/A | 1 | 3580634 | + | NA | NA | NA | NA | NA | NA | AG | AG | GG | AG | GG | ... | AA | | rs13475699 | G | 1 | 3860406 | + | NA | NA | NA | NA | NA | NA | GG | GG | GG | GG | GG | ... | GG |

# Full-featured function (Recommended)
MVP.Data(fileHMP="hapmap.txt",
         filePhe="Phenotype.txt",
         sep.hmp="\t",
         sep.phe="\t",
         SNP.effect="Add",
         fileKin=FALSE,
         filePC=FALSE,
         #maxLine=10000,
         out="mvp.hmp"
         )

# Only convert genotypes
MVP.Data.Hapmap2MVP("hapmap.txt", out='mvp') # the genotype data should be fully imputed before using this function

Numeric

back to top If you have genotype data in Numeric (m * n, m rows and n columns, m is the number of SNPs, n is the number of individuals) format:  

fileNum, name of genotype data in Numeric format filePhe, name of phenotype file fileMap, name of map file, a header should be added, e.g. SNP Chr Pos sep.num, separator of Numeric file sep.phe, separator of phenotype file type.geno, the type of data in Numeric file, "char", "integer", or "double" 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 auto_transpose, bool, if auto_transpose = TRUE, it is automatically transposed to ensure that the number of rows (markers) is greater than the number of columns (individuals).

Numeric.txt Map.txt 1 1 2 1 2 … 0 1 1 0 1 0 … 2 1 2 2 1 2 … 0 1 1 2 1 2 … 0 0 0 0 0 0 … 0 SNP Chr Pos rs3683945 1 3197400 rs3707673 1 3407393 rs6269442 1 3492195 rs6336442 1 3580634 rs13475699 1 3860406 

# Full-featured function (Recommended)
MVP.Data(fileNum="Numeric.txt",
         filePhe="Phenotype.txt",
         fileMap="Map.txt",
         sep.num="\t",
         sep.map="\t", 
         sep.phe="\t",
         fileKin=FALSE,
         filePC=FALSE,
         #maxLine=10000,
         out="mvp.num"
         )

# Only convert genotypes
MVP.Data.Numeric2MVP("Numeric.txt", out='mvp', maxLine=1e4, auto_transpose=T) # the genotype data should be fully imputed before using this function

Genomic relationship matrix (Optional)

back to top If you have Kinship matrix data that represents the relationship among individuals

fileKin, name of Kinship matrix data, the dimension is n * n (n is sample size), no taxa names included sep.kin, separator of Kinship file

mvp.kin.txt

0.3032 -0.0193 0.0094 0.0024 0.0381 ... -0.0072 -0.0193 0.274 -0.0243 0.0032 -0.0081 ... 0.0056 0.0094 -0.0243 0.3207 -0.0071 -0.0045 ... -0.0407 0.0024 0.0032 -0.0071 0.321 -0.008 ... -0.0093 0.0381 -0.0081 -0.0045 -0.008 0.3498 ... -0.0238 ... ... ... ... ... ... ... -0.0072 0.0056 -0.0407 -0.0093 -0.0238 ... 0.3436 
# read from file
MVP.Data.Kin("mvp.kin.txt", out="mvp", maxLine=1e4, sep='\t')

# calculate from mvp_geno_file
MVP.Data.Kin(TRUE, mvp_prefix='mvp.vcf', out='mvp')

Principal Components (Optional)

back to top If you have Principal Components data

filePC, name of Principal Components matrix data, the dimension is n * nPC (n is sample size, nPC is number of first columns of PCs), no taxa names and header row included sep.pc, separator of Principal Components file

mvp.pc.txt

0.010175524 -0.037989071 0.009588312 -0.009138673 -0.036763080 -0.006396714 -0.004723734 -0.047837625 0.021687731 0.012887843 -0.048418352 0.054298850 0.003871951 -0.038070387 0.008020508 -0.079505846 0.005818163 -0.206364549 
# read from file
MVP.Data.PC("mvp.pc.txt", out='mvp', sep='\t')

# calculate from mvp_geno_file
MVP.Data.PC(TRUE, mvp_prefix='mvp.vcf', pcs.keep=5)

Data Input

Basic

back to top At least you should prepare three datasets: genotype, phenotype, and map

genotype, genotype data generated by 'MVP.Data' function phenotype, phenotype data, the first column is taxa name and second column is phenotype value. 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 map, SNP map information, the first column is SNP name, the second column is Chromosome ID, the third column is phsical position 

genotype <- attach.big.matrix("mvp.geno.desc")
phenotype <- read.table("mvp.phe",head=TRUE)
map <- read.table("mvp.geno.map" , head = TRUE)

Advanced

back to top You can load the prepared Kinship matrix and Covariates data generated by 'MVP.Data' function Kinship, Kinship matrix, the dimension of Kinship matrix is n * n (n is sample size), no taxa names included Covariates, Covariates matrix, the dimension of Covariates matrix is n * nCV (n is sample size, nCV is number of covariates, no taxa names and header row included NOTE: If pcs have been added in covariate files, PLEASE DO NOT assign value to nPC.GLM, nPC.MLM, nPC.FarmCPU.

```r
Kinship <- attach.big.matrix("mvp.kin.desc")
Covariates_PC <- bigmemory::as.matrix(attach.big.matrix("mvp.pc.desc"))

If you have additional environmental fixed effects (e.g., breed, sex) or covariates (weight), please use it as following:

Covariates <- model.matrix.lm(~as.factor(breed)+as.factor(sex)+as.numeric(weight), data=yourdata, na.action = "na.pass")
# NA is acceptable in 'Covariates'

# if you are supposed to take PC to covariate
Covariates <- cbind(Covariates, Covariates_PC)

NOTE: rMVP has no function of adjusting the order of individuals in covariates. PLEASE make sure the order of individuals in covariates file must be consistent with that in genotype file.

If you have prepared Kinship matrix and Covariates data generated by other software packages, see Kinship and Principal Components

Start GWAS

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Three models are included in MVP package: General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU.

phe, phenotype data geno, genotype data map, map data K, Kinship matrix CV.GLM, Covariates added in GLM CV.MLM, Covariates added in MLM CV.FarmCPU, Covariates added in FarmCPU If you don't want to add PCs as covariates, please comment out the parameters instead of setting the nPC to 0 please attention that if nPC.GLM > 0, no PCs should be added in CV.GLM nPC.GLM, number of first columns of Principal Components added in GLM please attention that if nPC.MLM > 0, no PCs should be added in CV.MLM nPC.MLM, number of first columns of Principal Components added in MLM please attention that if nPC.FarmCPU > 0, no PCs should be added in CV.FarmCPU nPC.FarmCPU, number of first columns of Principal Components added in FarmCPU maxLine, the number of markers handled at a time, smaller value would reduce the memory cost ncpus, number of CPUs used for parallel computation, If not set, all CPUs will be used by default vc.method, methods of variance components analysis, three methods are avaiblable, "BRENT", "EMMA", and "HE" maxLoop, a parameter for FarmCPU only, the maximum iterations allowed in FarmCPU method.bin, a parameter for FarmCPU only, two options are available: 'static' or 'FaST-LMM' permutation.threshold, if TRUE, a threshold of permutation will be used in manhattan plot. The phenotypes are permuted to break the relationship with the genotypes. The experiment is replicated for a number of times. A vector of minimum p value of all experiments is recorded and the 95% quantile value of this vector is recommended to be used as significant threshold permutation.rep, number of permutation replicates, only used when permutation.threshold is TRUE threshold, 0.05/marker size, a cutoff line on manhattan plot method, models for association tests, three models are available in MVP, "GLM", "MLM", and "FarmCPU", one or two or three models can be selected for association tests file.output, a Boolean value or a string vector. If TRUE, output all types of files. If FALSE, no files are output. For string vectors, the available values are c("pmap", "pmap.signal", "plot", "log"). Among them, pmap represents all SNP P-Val files, pmap.signal represents significant SNP files, Plot represents visualization results, and log represents log files.

imMVP <- MVP(
    phe=phenotype,          #NA is acceptable in phenotype
    geno=genotype,
    map=map,
    #K=Kinship,             #if you have pre-computed GRM, please keep there open, otherwise rMVP will compute it automatically
    #CV.GLM=Covariates,     #if you have environmental covariates, please keep all 'CV.*' open
    #CV.MLM=Covariates,
    #CV.FarmCPU=Covariates,
    nPC.GLM=5,              #if you have added PCs into covariates, please keep there closed
    nPC.MLM=3,              #if you don't want to add PCs as covariates, please comment out the parameter instead of setting it to 0.
    nPC.FarmCPU=3,
    maxLine=10000,          #smaller value would reduce the memory cost
    #ncpus=10,
    vc.method="BRENT",      #only works for MLM
    method.bin="static",    # "FaST-LMM", "static" (#only works for FarmCPU)
    threshold=0.05,
    method=c("GLM", "MLM", "FarmCPU"),
    file.output=c("pmap", "pmap.signal", "plot", "log")
)

If you have more than one phenotype

for(i in 2:ncol(phenotype)){
  imMVP <- MVP(
    phe=phenotype[, c(1, i)],
    geno=genotype,
    map=map,
    #K=Kinship,
    #CV.GLM=Covariates,
    #CV.MLM=Covariates,
    #CV.FarmCPU=Covariates,
    nPC.GLM=5,
    nPC.MLM=3,
    nPC.FarmCPU=3,
    maxLine=10000,
    #ncpus=10,
    vc.method="BRENT",
    method.bin="static",
    threshold=0.05,
    method=c("GLM", "MLM", "FarmCPU"),
    file.output=c("pmap", "pmap.signal", "plot", "log")
  )
  gc()
}

Output

back to top MVP automatically outputs high-quality figures, three types of figure formats are available (".jpg",".pdf",".tiff", default is ".jpg"). Users could also adjust the output figure using about 50 parameters in MVP.Report(). MVP.Report() not only accept the final return of MVP(), but also accepts results from third-party software packages, such as PLINK, GEMMA, GAPIT, TASSEL, and FarmCPU. The result from third-party software packages should at least contain four columns, which are marker name, chromosome, physical position, and P-value of a trait, results of more than one trait could be sequentially appended column by column. Typing ?MVP.Report() to see details of all parameters and typing data(pig60K) or data(cattle50K) to load demo datasets. Type ?MVP.Repory to see parameter details.

> data(pig60K)   #GWAS result of MLM
> data(cattle50K)   #SNP effects calculated from rrblup

> head(pig60K)

          SNP Chromosome Position    trait1     trait2     trait3
1 ALGA0000009          1    52297 0.7738187 0.51194318 0.51194318
2 ALGA0000014          1    79763 0.7738187 0.51194318 0.51194318
3 ALGA0000021          1   209568 0.7583016 0.98405289 0.98405289
4 ALGA0000022          1   292758 0.7200305 0.48887140 0.48887140
5 ALGA0000046          1   747831 0.9736840 0.22096836 0.22096836
6 ALGA0000047          1   761957 0.9174565 0.05753712 0.05753712

> head(cattle50K)

   SNP chr    pos Somatic cell score  Milk yield Fat percentage
1 SNP1   1  59082        0.000244361 0.000484255    0.001379210
2 SNP2   1 118164        0.000532272 0.000039800    0.000598951
3 SNP3   1 177246        0.001633058 0.000311645    0.000279427
4 SNP4   1 236328        0.001412865 0.000909370    0.001040161
5 SNP5   1 295410        0.000090700 0.002202973    0.000351394
6 SNP6   1 354493        0.000110681 0.000342628    0.000105792

In the demo datasets, the first three columns are marker name, chromosome, and physical position, respectively, the rest columns are the P-value or effect of multiple traits. Number of traits is theoretically unlimited.

Phenotype distribution

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phe, phenotype data file.type, format of output figure breakNum, number of breaking points for phenotype when plotting distribution dpi, resolution of output figure 

MVP.Hist(phe=phenotype, file.type="jpg", breakNum=18, dpi=300)

SNP-density plot

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plot.type, four options ("d", "c", "m", "q"); if "d", draw SNP-density plot bin.size, the window size for counting SNP number bin.max, maximum SNP number, for windows, which has more SNPs than bin.max, will be painted in same color col, colors for separating windows with different SNP density file.type, format of output figure dpi, resolution of output figure 

MVP.Report(pig60K[, c(1:3)], plot.type="d", col=c("darkgreen", "yellow", "red"), file.type="jpg", dpi=300)

PCA plot

back to top pca, the first three columns of principle components Ncluster, cluster number class, the class of all individuals, for example: "breed", "location"... col, colors for each cluster pch, point shape for each cluster file.type, format of output figure dpi, resolution of output figure 

pca <- attach.big.matrix("mvp.pc.desc")[, 1:3]
#pca <- prcomp(t(as.matrix(genotype)))$x[, 1:3]
MVP.PCAplot(PCA=pca, Ncluster=3, class=NULL, col=c("red", "green", "yellow"), file.type="jpg")

Manhattan plot in Circular fashion

back to top For GWAS results:

> MVP.Report(pig60K,plot.type="c",chr.labels=paste("Chr",c(1:18,"X"),sep=""),r=0.4,cir.legend=TRUE,
        outward=FALSE,cir.legend.col="black",cir.chr.h=1.3,chr.den.col="black",file.type="jpg",
        memo="",dpi=300)


> MVP.Report(pig60K,plot.type="c",r=0.4,col=c("grey30","grey60"),chr.labels=paste("Chr",c(1:18,"X"),sep=""),
      threshold=c(1e-6,1e-4),cir.chr.h=1.5,amplify=TRUE,threshold.lty=c(1,2),threshold.col=c("red",
      "blue"),signal.line=1,signal.col=c("red","green"),chr.den.col=c("darkgreen","yellow","red"),
      bin.size=1e6,outward=FALSE,file.type="jpg",memo="",dpi=300)

#Note:
1. if signal.line=NULL, the lines that crosse circles won't be added.
2. if the length of parameter 'chr.den.col' is not equal to 1, SNP density that counts 
   the number of SNP within given size('bin.size') will be plotted around the circle.

For GS/GP results:

> MVP.Report(cattle50K,plot.type="c",LOG10=FALSE,outward=TRUE,matrix(c("#4DAF4A",NA,NA,"dodgerblue4",
            "deepskyblue",NA,"dodgerblue1", "olivedrab3", "darkgoldenrod1"), nrow=3, byrow=TRUE),
            chr.labels=paste("Chr",c(1:29),sep=""),threshold=NULL,r=1.2,cir.chr.h=1.5,cir.legend.cex=0.5,
            cir.band=1,file.type="jpg", memo="",dpi=300,chr.den.col="black")

#Note: 
Parameter 'col' can be either vector or matrix, if a matrix, each trait can be plotted in different colors.

Manhattan plot in Rectangular fashion for single trait or method

back to top For GWAS results:

> MVP.Report(pig60K,plot.type="m",LOG10=TRUE,threshold=NULL,col=c("dodgerblue4","deepskyblue"), cex=0.7,
            chr.den.col=NULL,file.type="jpg",memo="",dpi=300)


> MVP.Report(pig60K, plot.type="m", col=c("dodgerblue4","deepskyblue"), LOG10=TRUE, ylim=NULL,
        threshold=c(1e-6,1e-4), threshold.lty=c(1,2), threshold.lwd=c(1,1), threshold.col=c("black",
        "grey"), amplify=TRUE,chr.den.col=NULL, signal.col=c("red","green"), signal.cex=c(1,1),
        signal.pch=c(19,19),file.type="jpg",memo="",dpi=300)


> MVP.Report(pig60K, plot.type="m", LOG10=TRUE, ylim=NULL, threshold=c(1e-6,1e-4),threshold.lty=c(1,2),
        col=c("grey60","grey30"), threshold.lwd=c(1,1), threshold.col=c("black","grey"), amplify=TRUE,
        chr.den.col=c("darkgreen", "yellow", "red"),bin.size=1e6,signal.col=c("red","green"),
        signal.cex=c(1,1),signal.pch=c(19,19),file.type="jpg",memo="",dpi=300)

#Note:
if the length of parameter 'chr.den.col' is bigger than 1, SNP density that counts 
   the number of SNP within given size('bin.size') will be plotted.

For GS/GP results:

> MVP.Report(cattle50K, plot.type="m", band=0, LOG10=FALSE, ylab="Abs(SNP effect)",threshold=0.015,
        threshold.lty=2, threshold.lwd=1, threshold.col="red", amplify=TRUE, signal.col=NULL,
        col=c("dodgerblue4","deepskyblue"), chr.den.col=NULL, file.type="jpg",memo="",dpi=300)

#Note: 
if signal.col=NULL, the significant SNPs will be plotted with original colors.

Manhattan plot in Rectangular fashion for multiple traits or methods

back to top 

> MVP.Report(pig60K, plot.type="m", multracks=TRUE, threshold=c(1e-6,1e-4),threshold.lty=c(1,2), 
        threshold.lwd=c(1,1), threshold.col=c("black","grey"), amplify=TRUE,bin.size=1e6,
        chr.den.col=c("darkgreen", "yellow", "red"), signal.col=c("red","green"),signal.cex=c(1,1),
        file.type="jpg",memo="",dpi=300)

a. all traits in a axes:

b. all traits in separated axes:

Q-Q plot for single trait or method

back to top 

> MVP.Report(pig60K,plot.type="q",conf.int.col=NULL,box=TRUE,file.type="jpg",memo="",dpi=300)

Q-Q plot for multiple traits or methods

back to top 

> MVP.Report(imMVP,plot.type="q",col=c("dodgerblue1", "olivedrab3", "darkgoldenrod1"),threshold=1e6,
        signal.pch=19,signal.cex=1.5,signal.col="red",conf.int.col="grey",box=FALSE,multracks=
        TRUE,file.type="jpg",memo="",dpi=300)

Citation

back to top 

For MVP (Please cite MVP package for all the functions):
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.

For calculation of K matrix:
Vanraden, P. M. "Efficient Methods to Compute Genomic Predictions." Journal of Dairy Science 91.11(2008):4414-4423.

For GLM(PC) model:
Price, Alkes L., et al. "Principal components analysis corrects for stratification in genome-wide association studies." Nature genetics 38.8 (2006): 904.

For MLM(K) model:
Yu, Jianming, et al. "A unified mixed-model method for association mapping that accounts for multiple levels of relatedness." Nature genetics 38.2 (2006): 203.

For MLM(PCA+K) model:
Price, Alkes L., et al. "Principal components analysis corrects for stratification in genome-wide association studies." Nature genetics 38.8 (2006): 904.

For FarmCPU model:
Liu, Xiaolei, et al. "Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies." PLoS genetics 12.2 (2016): e1005767.

For variance components:
> HE: Zhou, Xiang. "A unified framework for variance component estimation with summary statistics in genome-wide association studies." The annals of applied statistics 11.4 (2017): 2027.
> EMMA/P3D:
1. Kang, Hyun Min, et al. "Efficient control of population structure in model organism association mapping." Genetics 178.3 (2008): 1709-1723.
2. Zhang, Zhiwu, et al. "Mixed linear model approach adapted for genome-wide association studies." Nature genetics 42.4 (2010): 355.
> Fast-lmm: Lippert, Christoph, et al. "FaST linear mixed models for genome-wide association studies." Nature methods 8.10 (2011): 833.

FAQ and Hints

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:sos: Question1: Failing to install "devtools":

ERROR: configuration failed for package ‘git2r’

removing ‘/Users/acer/R/3.4/library/git2r’

ERROR: dependency ‘git2r’ is not available for package ‘devtools’

removing ‘/Users/acer/R/3.4/library/devtools’

:yum: Answer: Please try following codes in terminal:

apt-get install libssl-dev/unstable

:sos: Question2: When installing packages from Github with "devtools", an error occurred:

Error in curl::curl_fetch_disk(url, x$path, handle = handle): Problem with the SSL CA cert (path? access rights?)

:yum: Answer: Please try following codes and then try agian.

library(httr)
set_config(config(ssl_verifypeer = 0L))

:sos: Question3: When installing MVP:

Error in lazyLoadDBinsertVariable(vars[i], from, datafile, ascii, compress, : write failed ERROR: lazy loading failed for package ‘MVP’ removing ‘/home/liuxl/R/x86_64-pc-linux-gnu-library/3.3/MVP’ Warning message: In install.packages("MVP_1.0.1.tar.gz", repos = NULL) : installation of package ‘MVP_1.0.1.tar.gz’ had non-zero exit status

:yum: Answer: It is probably an issue caused by disk full, please check disk space.

Questions, suggestions, and bug reports are welcome and appreciated. :arrow_right:

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XiaoleiLiuBio/MVP documentation built on Sept. 27, 2024, 7:44 a.m.

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