Package 'rMVP'

MVP, Memory-efficient, Visualization-enhanced, Parallel-accelerated

Description

Object 1: To perform GWAS using General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU model Object 2: To calculate kinship among individuals using Varaden method Object 3: Estimate variance components using EMMA, FaST-LMM, and HE regression Object 4: Generate high-quality figures

Usage

MVP(
  phe,
  geno,
  map,
  K = NULL,
  nPC.GLM = NULL,
  nPC.MLM = NULL,
  nPC.FarmCPU = NULL,
  CV.GLM = NULL,
  CV.MLM = NULL,
  CV.FarmCPU = NULL,
  REML = NULL,
  maxLine = 10000,
  ncpus = detectCores(logical = FALSE),
  vc.method = c("BRENT", "EMMA", "HE"),
  method = c("GLM", "MLM", "FarmCPU"),
  p.threshold = NA,
  QTN.threshold = 0.01,
  method.bin = "static",
  bin.size = c(5e+05, 5e+06, 5e+07),
  bin.selection = seq(10, 100, 10),
  maxLoop = 10,
  permutation.threshold = FALSE,
  permutation.rep = 100,
  memo = NULL,
  outpath = getwd(),
  col = c("#4197d8", "#f8c120", "#413496", "#495226", "#d60b6f", "#e66519", "#d581b7",
    "#83d3ad", "#7c162c", "#26755d"),
  file.output = TRUE,
  file.type = "jpg",
  dpi = 300,
  threshold = 0.05,
  verbose = TRUE
)

Arguments

phe	phenotype, n * 2 matrix, n is sample size
geno	Genotype in bigmatrix format; m * n, m is marker size, n is sample size
map	SNP map information, SNP name, Chr, Pos
K	Kinship, Covariance matrix(n * n) for random effects, must be positive semi-definite
nPC.GLM	number of PCs added as fixed effects in GLM
nPC.MLM	number of PCs added as fixed effects in MLM
nPC.FarmCPU	number of PCs added as fixed effects in FarmCPU
CV.GLM	covariates added in GLM
CV.MLM	covariates added in MLM
CV.FarmCPU	covariates added in FarmCPU
REML	a list contains ve and vg
maxLine	the number of markers handled at a time, smaller value would reduce the memory cost
ncpus	number of cpus used for parallel
vc.method	methods for estimating variance component("EMMA" or "HE" or "BRENT")
method	the GWAS model, "GLM", "MLM", and "FarmCPU", models can be selected simutaneously, i.e. c("GLM", "MLM", "FarmCPU")
p.threshold	if all p values generated in the first iteration are bigger than p.threshold, FarmCPU stops
QTN.threshold	in second and later iterations, only SNPs with lower p-values than QTN.threshold have chances to be selected as pseudo QTNs
method.bin	'static' or 'FaST-LMM'
bin.size	window size in genome
bin.selection	a vector, how many windows selected
maxLoop	maximum number of iterations
permutation.threshold	if use a permutation cutoff or not (bonferroni cutoff)
permutation.rep	number of permutation replicates
memo	Character. A text marker on output files
outpath	Effective only when file.output = TRUE, determines the path of the output file
col	for color of points in each chromosome on manhattan plot
file.output	whether to output files or not
file.type	figure formats, "jpg", "tiff"
dpi	resolution for output figures
threshold	a cutoff line on manhattan plot, 0.05/marker size
verbose	whether to print detail.

Details

Build date: Aug 30, 2017 Last update: Dec 14, 2018

Value

a m * 2 matrix, the first column is the SNP effect, the second column is the P values Output: MVP.return$map - SNP map information, SNP name, Chr, Pos Output: MVP.return$glm.results - p-values obtained by GLM method Output: MVP.return$mlm.results - p-values obtained by MLM method Output: MVP.return$farmcpu.results - p-values obtained by FarmCPU method

Author(s)

Lilin Yin, Haohao Zhang, and Xiaolei Liu

Examples

phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP")
phenotype <- read.table(phePath, header=TRUE)
print(dim(phenotype))
genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
genotype <- attach.big.matrix(genoPath)
print(dim(genotype))
mapPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.map", package = "rMVP")
map <- read.table(mapPath , head = TRUE)

opts <- options(rMVP.OutputLog2File = FALSE)

mvp <- MVP(phe=phenotype, geno=genotype, map=map, maxLoop=3,
  method=c("GLM", "MLM", "FarmCPU"), file.output=FALSE, ncpus=1)
str(mvp)

options(opts)

---

MVP.BRENT.Vg.Ve variance component estimation using the BRENT method

Description

MVP.BRENT.Vg.Ve variance component estimation using the BRENT method

Usage

MVP.BRENT.Vg.Ve(y, X, eigenK, verbose = FALSE)

Arguments

y	phenotype
X	covariate matrix, the first column is 1s
eigenK	eigen of Kinship matrix
verbose	whether to print detail.

Value

vg, ve, and delta

Examples

phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP")
phenotype <- read.table(phePath, header=TRUE)
print(dim(phenotype))
genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
genotype <- attach.big.matrix(genoPath)
print(dim(genotype))

eigenK <- eigen(MVP.K.VanRaden(genotype, cpu=1))
vc <- MVP.BRENT.Vg.Ve(y=phenotype[,2], X=matrix(1, nrow(phenotype)), eigenK=eigenK)
print(vc)

---

MVP.Data: To prepare data for MVP package Author: Xiaolei Liu, Lilin Yin and Haohao Zhang Build date: Aug 30, 2016 Last update: Sep 12, 2018

Description

MVP.Data: To prepare data for MVP package Author: Xiaolei Liu, Lilin Yin and Haohao Zhang Build date: Aug 30, 2016 Last update: Sep 12, 2018

Usage

MVP.Data(
  fileMVP = NULL,
  fileVCF = NULL,
  fileHMP = NULL,
  fileBed = NULL,
  fileNum = NULL,
  fileMap = NULL,
  filePhe = NULL,
  fileInd = NULL,
  fileKin = NULL,
  filePC = NULL,
  out = "mvp",
  sep.num = "\t",
  auto_transpose = TRUE,
  sep.map = "\t",
  sep.phe = "\t",
  sep.kin = "\t",
  sep.pc = "\t",
  type.geno = "char",
  pheno_cols = NULL,
  SNP.impute = "Major",
  maxLine = 10000,
  pcs.keep = 5,
  verbose = TRUE,
  ncpus = NULL,
  ...
)

Arguments

fileMVP	Genotype in MVP format
fileVCF	Genotype in VCF format
fileHMP	Genotype in hapmap format
fileBed	Genotype in PLINK binary format
fileNum	Genotype in numeric format; pure 0, 1, 2 matrix; m * n, m is marker size, n is sample size
fileMap	SNP map information, there are three columns, including SNP_ID, Chromosome, and Position
filePhe	Phenotype, the first column is taxa name, the subsequent columns are traits
fileInd	Individual name file
fileKin	Kinship that represents relationship among individuals, n * n matrix, n is sample size
filePC	Principal components, n*npc, n is sample size, npc is number of top columns of principal components
out	prefix of output file name
sep.num	seperator for numeric file.
auto_transpose	Whether to automatically transpose numeric genotypes, the default is True, which will identify the most one of the rows or columns as a marker, If set to False, the row represents the marker and the column represents the individual.
sep.map	seperator for map file.
sep.phe	seperator for phenotype file.
sep.kin	seperator for Kinship file.
sep.pc	seperator for PC file.
type.geno	type parameter in bigmemory, genotype data. The default is char, it is highly recommended *NOT* to modify this parameter.
pheno_cols	Extract which columns of the phenotype file (including individual IDs)
SNP.impute	"Left", "Middle", "Right", or NULL for skip impute.
maxLine	the number of markers handled at a time, smaller value would reduce the memory cost
pcs.keep	how many PCs to keep
verbose	whether to print detail.
ncpus	The number of threads used, if NULL, (logical core number - 1) is automatically used
...	Compatible with DEPRECATED parameters.

Value

NULL Output files: genotype.desc, genotype.bin: genotype file in bigmemory format phenotype.phe: ordered phenotype file, same taxa order with genotype file map.map: SNP information k.desc, k.bin: Kinship matrix in bigmemory format pc.desc, pc.bin: PC matrix in bigmemory format Requirement: fileHMP, fileBed, and fileNum can not input at the same time

Examples

bfilePath <- file.path(system.file("extdata", "02_bfile", package = "rMVP"), "mvp")
opts <- options(rMVP.OutputLog2File = FALSE)

MVP.Data(fileBed=bfilePath, out=tempfile("outfile"), ncpus=1)

options(opts)

---

MVP.Data.Bfile2MVP: To transform plink binary data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018

Description

MVP.Data.Bfile2MVP: To transform plink binary data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018

Usage

MVP.Data.Bfile2MVP(
  bfile,
  out = "mvp",
  maxLine = 10000,
  type.geno = "char",
  threads = 0,
  verbose = TRUE
)

Arguments

bfile	Genotype in binary format (.bed, .bim, .fam)
out	the name of output file
maxLine	the number of markers handled at a time, smaller value would reduce the memory cost
type.geno	the type of genotype elements
threads	number of thread for transforming
verbose	whether to print the reminder

Value

number of individuals and markers. Output files: genotype.desc, genotype.bin: genotype file in bigmemory format phenotype.phe: ordered phenotype file, same taxa order with genotype file map.map: SNP information

Examples

bfilePath <- file.path(system.file("extdata", "02_bfile", package = "rMVP"), "mvp")

MVP.Data.Bfile2MVP(bfilePath, tempfile("outfile"), threads=1)

---

MVP.Data.Hapmap2MVP: To transform Hapmap data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018

Description

MVP.Data.Hapmap2MVP: To transform Hapmap data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018

Usage

MVP.Data.Hapmap2MVP(
  hmp_file,
  out = "mvp",
  maxLine = 10000,
  type.geno = "char",
  threads = 1,
  verbose = TRUE
)

Arguments

hmp_file	Genotype in Hapmap format
out	the name of output file
maxLine	the number of markers handled at a time, smaller value would reduce the memory cost
type.geno	the type of genotype elements
threads	number of thread for transforming
verbose	whether to print the reminder

Value

number of individuals and markers. Output files: genotype.desc, genotype.bin: genotype file in bigmemory format phenotype.phe: ordered phenotype file, same taxa order with genotype file map.map: SNP information

Examples

hapmapPath <- system.file("extdata", "03_hapmap", "mvp.hmp.txt", package = "rMVP")

MVP.Data.Hapmap2MVP(hapmapPath, tempfile("outfile"), threads=1)

---

MVP.Data.impute: To impute the missing genotype Author: Haohao Zhang Build date: Sep 12, 2018

Description

MVP.Data.impute: To impute the missing genotype Author: Haohao Zhang Build date: Sep 12, 2018

Usage

MVP.Data.impute(
  mvp_prefix,
  out = NULL,
  method = "Major",
  ncpus = NULL,
  verbose = TRUE
)

Arguments

mvp_prefix	the prefix of mvp file
out	the prefix of output file
method	'Major', 'Minor', "Middle"
ncpus	number of threads for imputing
verbose	whether to print the reminder

Value

NULL Output files: imputed genotype file

Examples

mvpPath <- file.path(system.file("extdata", "05_mvp", package = "rMVP"), "mvp")

MVP.Data.impute(mvpPath, tempfile("outfile"), ncpus=1)

---

Kinship

Description

Kinship

Usage

MVP.Data.Kin(
  fileKin = TRUE,
  mvp_prefix = "mvp",
  out = NULL,
  maxLine = 10000,
  sep = "\t",
  cpu = 1,
  verbose = TRUE
)

Arguments

fileKin	Kinship that represents relationship among individuals, n * n matrix, n is sample size
mvp_prefix	Prefix for mvp format files
out	prefix of output file name
maxLine	the number of markers handled at a time, smaller value would reduce the memory cost
sep	seperator for Kinship file.
cpu	the number of cpu
verbose	whether to print detail.

Value

Output file: <out>.kin.bin <out>.kin.desc

Examples

geno <- file.path(system.file("extdata", "06_mvp-impute", package = "rMVP"), "mvp.imp")

MVP.Data.Kin(TRUE, mvp_prefix=geno, out=tempfile("outfile"), cpu=1)

---

MVP.Data.Map: To check map file Author: Haohao Zhang Build date: Sep 12, 2018

Description

MVP.Data.Map: To check map file Author: Haohao Zhang Build date: Sep 12, 2018

Usage

MVP.Data.Map(
  map,
  out = "mvp",
  cols = 1:5,
  header = TRUE,
  sep = "\t",
  verbose = TRUE
)

Arguments

map	the name of map file or map object(data.frame or matrix)
out	the name of output file
cols	selected columns
header	whether the file contains header
sep	seperator of the file
verbose	whether to print detail.

Value

Output file: <out>.map

Examples

mapPath <- system.file("extdata", "05_mvp", "mvp.geno.map", package = "rMVP")

MVP.Data.Map(mapPath, tempfile("outfile"))

---

MVP.Data.MVP2Bfile: To transform MVP data to binary format Author: Haohao Zhang Build date: Sep 12, 2018

Description

MVP.Data.MVP2Bfile: To transform MVP data to binary format Author: Haohao Zhang Build date: Sep 12, 2018

Usage

MVP.Data.MVP2Bfile(
  bigmat,
  map,
  pheno = NULL,
  out = "mvp.plink",
  threads = 1,
  verbose = TRUE
)

Arguments

bigmat	Genotype in bigmatrix format (0,1,2)
map	the map file
pheno	the phenotype file
out	the name of output file
threads	number of thread for transforming
verbose	whether to print the reminder

Value

NULL Output files: .bed, .bim, .fam

Examples

bigmat <- as.big.matrix(matrix(1:6, 3, 2))
map <- matrix(c("rs1", "rs2", "rs3", 1, 1, 1, 10, 20, 30), 3, 3)

MVP.Data.MVP2Bfile(bigmat, map, out=tempfile("outfile"), threads=1)

---

MVP.Data.Numeric2MVP: To transform Numeric data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018

Description

MVP.Data.Numeric2MVP: To transform Numeric data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018

Usage

MVP.Data.Numeric2MVP(
  num_file,
  map_file,
  out = "mvp",
  maxLine = 10000,
  row_names = FALSE,
  col_names = FALSE,
  type.geno = "char",
  auto_transpose = TRUE,
  verbose = TRUE
)

Arguments

num_file	Genotype in Numeric format (0,1,2)
map_file	Genotype map file, SNP_name, Chr, Pos
out	the name of output file
maxLine	the number of markers handled at a time, smaller value would reduce the memory cost
row_names	whether the numeric genotype has row names
col_names	whether the numeric genotype has column names
type.geno	the type of genotype elements
auto_transpose	whether to detecte the row and column
verbose	whether to print the reminder

Value

number of individuals and markers. Output files: genotype.desc, genotype.bin: genotype file in bigmemory format phenotype.phe: ordered phenotype file, same taxa order with genotype file map.map: SNP information

Examples

numericPath <- system.file("extdata", "04_numeric", "mvp.num", package = "rMVP")
mapPath <- system.file("extdata", "04_numeric", "mvp.map", package = "rMVP")
MVP.Data.Numeric2MVP(numericPath, mapPath, tempfile("outfile"))

---

Principal component analysis

Description

Principal component analysis

Usage

MVP.Data.PC(
  filePC = TRUE,
  mvp_prefix = "mvp",
  K = NULL,
  out = NULL,
  pcs.keep = 5,
  maxLine = 10000,
  sep = "\t",
  cpu = 1,
  verbose = TRUE
)

Arguments

filePC	Principal components, n*npc, n is sample size, npc is number of top columns of principal components
mvp_prefix	Prefix for mvp format files
K	Kinship matrix
out	prefix of output file name
pcs.keep	how many PCs to keep
maxLine	the number of markers handled at a time, smaller value would reduce the memory cost
sep	seperator for PC file.
cpu	the number of cpu
verbose	whether to print detail.

Value

Output file: <out>.pc.bin <out>.pc.desc

Examples

geno <- file.path(system.file("extdata", "06_mvp-impute", package = "rMVP"), "mvp.imp")

MVP.Data.PC(TRUE, mvp_prefix=geno, out=tempfile("outfile"), cpu=1)

---

MVP.Data.Pheno: To clean up phenotype file Author: Haohao Zhang Build date: Sep 12, 2018

Description

MVP.Data.Pheno: To clean up phenotype file Author: Haohao Zhang Build date: Sep 12, 2018

Usage

MVP.Data.Pheno(
  pheno_file,
  out = "mvp",
  cols = NULL,
  header = TRUE,
  sep = "\t",
  missing = c(NA, "NA", "-9", 9999),
  verbose = TRUE
)

Arguments

pheno_file	the name of phenotype file
out	the name of output file
cols	selected columns
header	whether the file contains header
sep	seperator of the file
missing	the missing value
verbose	whether to print detail.

Value

NULL Output files: cleaned phenotype file

Examples

phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP")

MVP.Data.Pheno(phePath, out=tempfile("outfile"))

---

MVP.Data.VCF2MVP: To transform vcf data to MVP package Author: Haohao Zhang Build date: Sep 12, 2018

Description

Accept the | or / separated markers, any variant sites that are not 0 or 1 will be considered NA.

Usage

MVP.Data.VCF2MVP(
  vcf_file,
  out = "mvp",
  maxLine = 10000,
  type.geno = "char",
  threads = 1,
  verbose = TRUE
)

Arguments

vcf_file	Genotype in VCF format
out	the name of output file
maxLine	the number of markers handled at a time, smaller value would reduce the memory cost
type.geno	the type of genotype elements
threads	number of thread for transforming
verbose	whether to print the reminder

Value

number of individuals and markers. Output files: genotype.desc, genotype.bin: genotype file in bigmemory format phenotype.phe: ordered phenotype file, same taxa order with genotype file map.map: SNP information

Examples

vcfPath <- system.file("extdata", "01_vcf", "mvp.vcf", package = "rMVP")

MVP.Data.VCF2MVP(vcfPath, tempfile("outfile"), threads=1)

---

Estimate variance components using EMMA

Description

Build date: August 30, 2016 Last update: January 27, 2017

Usage

MVP.EMMA.Vg.Ve(y, X, K, ngrids = 100, llim = -10, ulim = 10, esp = 1e-10)

Arguments

y	phenotype, n * 2
X	covariate matrix, the first column is 1s
K	Kinship matrix
ngrids	parameters for estimating vg and ve
llim	parameters for estimating vg and ve
ulim	parameters for estimating vg and ve
esp	parameters for estimating vg and ve

Value

Output: REML - maximum log likelihood Output: delta - exp(root) Output: ve - residual variance Output: vg - genetic variance

Author(s)

EMMA (Kang et. al. Genetics, 2008), Modified only for speed up by Xiaolei Liu and Lilin Yin

Examples

phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP")
phenotype <- read.table(phePath, header=TRUE)
print(dim(phenotype))
genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
genotype <- attach.big.matrix(genoPath)
print(dim(genotype))

K <- MVP.K.VanRaden(genotype, cpu=1)
vc <- MVP.EMMA.Vg.Ve(y=phenotype[,2], X=matrix(1, nrow(phenotype)), K=K)
print(vc)

---

Perform GWAS using FarmCPU method

Description

Date build: Febuary 24, 2013 Last update: May 25, 2017 Requirement: Y, GD, and CV should have same taxa order. GD and GM should have the same order on SNPs

Usage

MVP.FarmCPU(
  phe,
  geno,
  map,
  CV = NULL,
  geno_ind_idx = NULL,
  P = NULL,
  method.sub = "reward",
  method.sub.final = "reward",
  method.bin = c("EMMA", "static", "FaST-LMM"),
  bin.size = c(5e+05, 5e+06, 5e+07),
  bin.selection = seq(10, 100, 10),
  memo = "MVP.FarmCPU",
  Prior = NULL,
  ncpus = 2,
  maxLoop = 10,
  threshold.output = 0.01,
  converge = 1,
  iteration.output = FALSE,
  p.threshold = NA,
  QTN.threshold = 0.01,
  bound = NULL,
  verbose = TRUE
)

Arguments

phe	phenotype, n by t matrix, n is sample size, t is number of phenotypes
geno	genotype, m by n matrix, m is marker size, n is sample size. This is Pure Genotype Data Matrix(GD). THERE IS NO COLUMN FOR TAXA.
map	SNP map information, m by 3 matrix, m is marker size, the three columns are SNP_ID, Chr, and Pos
CV	covariates, n by c matrix, n is sample size, c is number of covariates
geno_ind_idx	the index of effective genotyped individuals
P	start p values for all SNPs
method.sub	method used in substitution process, five options: 'penalty', 'reward', 'mean', 'median', or 'onsite'
method.sub.final	method used in substitution process, five options: 'penalty', 'reward', 'mean', 'median', or 'onsite'
method.bin	method for selecting the most appropriate bins, three options: 'static', 'EMMA' or 'FaST-LMM'
bin.size	bin sizes for all iterations, a vector, the bin size is always from large to small
bin.selection	number of selected bins in each iteration, a vector
memo	a marker on output file name
Prior	prior information, four columns, which are SNP_ID, Chr, Pos, P-value
ncpus	number of threads used for parallele computation
maxLoop	maximum number of iterations
threshold.output	only the GWAS results with p-values lower than threshold.output will be output
converge	a number, 0 to 1, if selected pseudo QTNs in the last and the second last iterations have a certain probality (the probability is converge) of overlap, the loop will stop
iteration.output	whether to output results of all iterations
p.threshold	if all p values generated in the first iteration are bigger than p.threshold, FarmCPU stops
QTN.threshold	in second and later iterations, only SNPs with lower p-values than QTN.threshold have chances to be selected as pseudo QTNs
bound	maximum number of SNPs selected as pseudo QTNs in each iteration
verbose	whether to print detail.

Value

a m by 4 results matrix, m is marker size, the four columns are SNP_ID, Chr, Pos, and p-value

Author(s)

Xiaolei Liu and Zhiwu Zhang

Examples

phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP")
phenotype <- read.table(phePath, header=TRUE)
idx <- !is.na(phenotype[, 2])
phenotype <- phenotype[idx, ]
print(dim(phenotype))
genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
genotype <- attach.big.matrix(genoPath)
genotype <- deepcopy(genotype, cols=idx)
print(dim(genotype))
mapPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.map", package = "rMVP")
map <- read.table(mapPath , head = TRUE)

farmcpu <- MVP.FarmCPU(phe=phenotype,geno=genotype,map=map,maxLoop=2,method.bin="static")
str(farmcpu)

---

Evaluation of the maximum likelihood using FaST-LMM method

Description

Last update: January 11, 2017

Usage

MVP.FaSTLMM.LL(pheno, snp.pool, X0 = NULL, ncpus = 2)

Arguments

pheno	a two-column phenotype matrix
snp.pool	matrix for pseudo QTNs
X0	covariates matrix
ncpus	number of threads used for parallel computation

Value

Output: beta - beta effect Output: delta - delta value Output: LL - log-likelihood Output: vg - genetic variance Output: ve - residual variance

Author(s)

Xiaolei Liu (modified)

---

To perform GWAS with GLM and MLM model and get the P value of SNPs

Description

Build date: Aug 30, 2016 Last update: May 25, 2017

Usage

MVP.GLM(phe, geno, CV = NULL, geno_ind_idx = NULL, cpu = 1, verbose = TRUE)

Arguments

phe	phenotype, n * 2 matrix
geno	Genotype in numeric format, pure 0, 1, 2 matrix; m * n, m is marker size, n is population size
CV	Covariance, design matrix(n * x) for the fixed effects
geno_ind_idx	the index of effective genotyped individuals
cpu	number of cpus used for parallel computation
verbose	whether to print detail.

Value

m * 2 matrix, the first column is the SNP effect, the second column is the P values

Author(s)

Lilin Yin and Xiaolei Liu

Examples

phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP")
phenotype <- read.table(phePath, header=TRUE)
idx <- !is.na(phenotype[, 2])
phenotype <- phenotype[idx, ]
print(dim(phenotype))
genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
genotype <- attach.big.matrix(genoPath)
genotype <- deepcopy(genotype, cols=idx)
print(dim(genotype))

glm <- MVP.GLM(phe=phenotype, geno=genotype, cpu=1)
str(glm)

---

To estimate variance component using HE regression

Description

Build date: Feb 2, 2017 Last update: Feb 2, 2019

Usage

MVP.HE.Vg.Ve(y, X, K)

Arguments

y	phenotype
X	genotype
K	kinship matrix

Value

vg, ve, and delta

Author(s)

Translated from C++(GEMMA, Xiang Zhou) to R by: Haohao Zhang

Examples

phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP")
phenotype <- read.table(phePath, header=TRUE)
print(dim(phenotype))
genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
genotype <- attach.big.matrix(genoPath)
print(dim(genotype))

K <- MVP.K.VanRaden(genotype, cpu=1)
vc <- MVP.HE.Vg.Ve(y=phenotype[,2], X=matrix(1, nrow(phenotype)), K=K)
print(vc)

---

Phenotype distribution histogram

Description

Phenotype distribution histogram

Usage

MVP.Hist(
  phe,
  col = c("dodgerblue4", "olivedrab4", "violetred", "darkgoldenrod1", "purple4"),
  breakNum = 15,
  memo = NULL,
  outpath = getwd(),
  test.method = "auto",
  file.type = "pdf",
  file.output = TRUE,
  dpi = 300
)

Arguments

phe	phenotype data
col	The color vector of the histogram. If the number of colors is less than break.n, the color will be reused. If the number of colors is greater than break.n, only the previous break.n colors will be used.
breakNum	the number of cells for the histogram. The default value is 15.
memo	Character. A text marker on output files
outpath	Effective only when file.output = TRUE, determines the path of the output file
test.method	The method used to test the normal distribution. The options are "auto", "Shapiro-Wilk", "Kolmogorov-Smirnov", and NULL. When set to "auto", "Shapiro- Wilk" method, "Kolmogorov-Smirnov" method will be used when it is greater than 5000, and it will not be tested when set to NULL.
file.type	A string or NULL is used to determine the type of output file. Can be "jpg", "pdf", "tiff". If it is NULL, it will use dev.new() to create a new graphics device in the current environment, which may be RStudioGD or the default device of the system.
file.output	Logical value. If TRUE, the figures will be generated.
dpi	The resolution of the image, specifying how many pixels per inch.

Value

Output file: MVP.Phe_Distribution.<trait>.<type>

Examples

phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP")
phe <- read.table(phePath, header=TRUE)

MVP.Hist(phe, file.output = FALSE)

---

Calculate Kinship matrix by VanRaden method

Description

Build date: Dec 12, 2016 Last update: Dec 12, 2019

Usage

MVP.K.VanRaden(
  M,
  maxLine = 5000,
  ind_idx = NULL,
  cpu = 1,
  verbose = TRUE,
  checkNA = TRUE
)

Arguments

M	Genotype, m * n, m is marker size, n is population size
maxLine	the number of markers handled at a time, smaller value would reduce the memory cost
ind_idx	the index of effective genotyped individuals used in analysis
cpu	the number of cpu
verbose	whether to print detail.
checkNA	whether to check NA in genotype.

Value

K, n * n matrix

Examples

genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
genotype <- attach.big.matrix(genoPath)
print(dim(genotype))

K <- MVP.K.VanRaden(genotype, cpu=1)

---

To perform GWAS with GLM and MLM model and get the P value of SNPs

Description

Build date: Aug 30, 2016 Last update: Aug 30, 2016

Usage

MVP.MLM(
  phe,
  geno,
  K = NULL,
  eigenK = NULL,
  CV = NULL,
  geno_ind_idx = NULL,
  REML = NULL,
  cpu = 1,
  vc.method = c("BRENT", "EMMA", "HE"),
  verbose = TRUE
)

Arguments

phe	phenotype, n * 2 matrix
geno	genotype, m * n, m is marker size, n is population size
K	Kinship, Covariance matrix(n * n) for random effects; must be positive semi-definite
eigenK	list of eigen Kinship
CV	covariates
geno_ind_idx	the index of effective genotyped individuals
REML	a list that contains ve and vg
cpu	number of cpus used for parallel computation
vc.method	the methods for estimating variance component("emma" or "he" or "brent")
verbose	whether to print detail.

Value

results: a m * 2 matrix, the first column is the SNP effect, the second column is the P values

Author(s)

Lilin Yin and Xiaolei Liu

Examples

phePath <- system.file("extdata", "07_other", "mvp.phe", package = "rMVP")
phenotype <- read.table(phePath, header=TRUE)
idx <- !is.na(phenotype[, 2])
phenotype <- phenotype[idx, ]
print(dim(phenotype))
genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
genotype <- attach.big.matrix(genoPath)
genotype <- deepcopy(genotype, cols=idx)
print(dim(genotype))
K <- MVP.K.VanRaden(genotype, cpu=1)

mlm <- MVP.MLM(phe=phenotype, geno=genotype, K=K, cpu=1)
str(mlm)

---

Principal Component Analysis

Description

Build date: Dec 14, 2016 Last update: Oct 29, 2018

Usage

MVP.PCA(
  M = NULL,
  K = NULL,
  maxLine = 10000,
  ind_idx = NULL,
  pcs.keep = 5,
  cpu = 1,
  verbose = TRUE
)

Arguments

M	Genotype in numeric format, pure 0, 1, 2 matrix; m * n, m is marker size, n is population size
K	kinship matrix
maxLine	the number of markers handled at a time, smaller value would reduce the memory cost
ind_idx	the index of effective genotyped individuals used in analysis
pcs.keep	maximum number of PCs for output
cpu	the number of cpu
verbose	whether to print detail.

Value

Output: PCs - a n * npc matrix of top number of PCs, n is population size and npc is @param pcs.keep

Author(s)

Xiaolei Liu, Lilin Yin and Haohao Zhang

Examples

genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
genotype <- attach.big.matrix(genoPath)
print(dim(genotype))

pca <- MVP.PCA(M=genotype, cpu=1)
str(pca)

---

PCA Plot

Description

PCA Plot

Usage

MVP.PCAplot(
  PCA,
  memo = "MVP",
  col = NULL,
  pch = NULL,
  class = NULL,
  legend.pos = "topright",
  Ncluster = 1,
  plot3D = FALSE,
  file.type = "pdf",
  dpi = 300,
  box = FALSE,
  file.output = TRUE,
  outpath = getwd(),
  verbose = TRUE
)

Arguments

PCA	Principal component analysis result, 2-column matrix
memo	the prefix of the output image file.
col	colors for each cluster
pch	Either an integer specifying a symbol or a single character to be used as the default in plotting points. See points for possible values and their interpretation. Note that only integers and single-character strings can be set as a graphics parameter (and not NA nor NULL).
class	the class of all individuals, for example: "breed", "location"
legend.pos	position of legend. default is "topright"
Ncluster	cluster number
plot3D	(DEPRECATED)if TRUE, plot PC figure in 3D format, it can be only used in windows and mac operation system, "rgl" package should be installed beforehead
file.type	Character. Options are jpg, pdf, and tiff
dpi	Number. Dots per inch for .jpg and .tiff files
box	Logical value. If TRUE, the border line of Manhattan plot will be added
file.output	Logical value. If TRUE, the figures will be generated.
outpath	Effective only when file.output = TRUE, determines the path of the output file
verbose	whether to print detail.

Value

Output file: MVP.PCA_2D.<type>

Examples

genoPath <- system.file("extdata", "06_mvp-impute", "mvp.imp.geno.desc", package = "rMVP")
geno <- attach.big.matrix(genoPath)
pca <- MVP.PCA(M=geno, cpu=1)

MVP.PCAplot(PCA=pca, Ncluster=3, class=NULL, 
    col=c("red", "green", "yellow"), file.output=FALSE, pch=19)

---

MVP.Report

Description

MVP.Report

Usage

MVP.Report(
  MVP,
  col = c("#4197d8", "#f8c120", "#413496", "#495226", "#d60b6f", "#e66519", "#d581b7",
    "#83d3ad", "#7c162c", "#26755d"),
  bin.size = 1e+06,
  bin.range = NULL,
  pch = 19,
  band = 1,
  H = 1.5,
  ylim = NULL,
  cex.axis = 1,
  lwd.axis = 1.5,
  cex.lab = 1.5,
  plot.type = "b",
  multracks = FALSE,
  cex = c(0.5, 1, 1),
  r = 0.3,
  xlab = "Chromosome",
  ylab = expression(-log[10](italic(p))),
  xaxs = "i",
  yaxs = "r",
  outward = FALSE,
  threshold = NULL,
  threshold.col = "red",
  threshold.lwd = 1,
  threshold.lty = 2,
  amplify = FALSE,
  signal.cex = 1.5,
  signal.pch = 19,
  signal.col = "red",
  signal.line = 1,
  highlight = NULL,
  highlight.cex = 1.5,
  highlight.pch = 19,
  highlight.col = "green",
  chr.labels = NULL,
  chr.den.col = "black",
  cir.band = 1,
  cir.chr = TRUE,
  cir.chr.h = 1.5,
  cir.legend = TRUE,
  cir.legend.cex = 0.6,
  cir.legend.col = "black",
  LOG10 = TRUE,
  box = FALSE,
  conf.int = TRUE,
  file.output = TRUE,
  outpath = getwd(),
  file.type = "jpg",
  dpi = 300,
  height = NULL,
  width = NULL,
  memo = "",
  verbose = TRUE
)

Arguments

MVP	a dataframe or list, at least four columns. The first column is the name of SNP, the second column is the chromosome of SNP, the third column is the position of SNP, and the remaining columns are the P-value of each trait(Note:each trait a column).
col	a vector or a matrix, if "col" is a vector, each circle use the same colors, it means that the same chromosome is drewed in the same color, the colors are not fixed, one, two, three or more colors can be used, if the length of the "col" is shorter than the length the chromosome, then colors will be applied circularly. If "col" is a matrix, the row is the number of circles(traits), the columns are the colors that users want to use for different circles, each circle can be plotted in different number of colors, the missing value can be replaced by NA. For example: col=matrix(c("grey30","grey60",NA,"red","blue","green","orange",NA,NA),3,3,byrow=T).
bin.size	the size of bin for SNP_density plot.
bin.range	a vector, c(min, max). The min/max value of legend of SNP_density plot, the bin whose SNP number is smaller/bigger than 'bin.range' will be use the same color.
pch	a number, the type for the points or for traits of multi-traits Manhattan plot, is the same with "pch" in <plot>.
band	a number, the space between chromosomes, the default is 1(if the band equals to 0, then there would be no space between chromosomes).
H	a number, the height for each circle, each circle represents a trait, the default is 1.
ylim	a vector, the range of Y-axis when plotting the two type of Manhattan plots, is the same with "ylim" in <plot>.
cex.axis	a number, controls the size of ticks' numbers of X/Y-axis and the size of labels of circle plot.
lwd.axis	a number, controls the width of X/Y-axis lines.
cex.lab	a number, controls the size of labels of X/Y-axis.
plot.type	a character or vector, only "d", "c", "m", "q" or "b" can be used. if plot.type="d", SNP density will be plotted; if plot.type="c", only circle-Manhattan plot will be plotted; if plot.type="m",only Manhattan plot will be plotted; if plot.type="q",only Q-Q plot will be plotted;if plot.type="b", both circle-Manhattan, Manhattan and Q-Q plots will be plotted; if plot.type=c("m","q"), Both Manhattan and Q-Q plots will be plotted.
multracks	a logical,if multracks=FALSE, all Manhattan plots will be drew in separated files, if it is TRUE, all Manhattan plots will be plotted in only one file.
cex	a number or a vector, the size for the points, is the same with "size" in <plot>, and if it is a vector, the first number controls the size of points in circle plot(the default is 0.5), the second number controls the size of points in Manhattan plot(the default is 1), the third number controls the size of points in Q-Q plot(the default is 1)
r	a number, the radius for the circle(the inside radius), the default is 1.
xlab	a character, the labels for x axis.
ylab	a character, the labels for y axis.
xaxs	a character, The style of axis interval calculation to be used for the x-axis. Possible values are "r", "i", "e", "s", "d". The styles are generally controlled by the range of data or xlim, if given.
yaxs	a character, The style of axis interval calculation to be used for the y-axis. See xaxs above..
outward	logical, if outward=TRUE,then all points will be plotted from inside to outside for circular Manhattan plot.
threshold	a number or vector, the significant threshold. For example, Bonfferoni adjustment method: threshold=0.01/nrow(Pmap). More than one significant line can be added on the plots, if threshold=0 or NULL, then the threshold line will not be added.
threshold.col	a character or vector, the colour for the line of threshold levels.
threshold.lwd	a number or vector, the width for the line of threshold levels.
threshold.lty	a number or vector, the type for the line of threshold levels.
amplify	logical, CMplot can amplify the significant points, if amplify=T, then the points bigger than the minimal significant level will be amplified, the default: amplify=TRUE.
signal.cex	a number, if amplify=TRUE, users can set the size of significant points.
signal.pch	a number, if amplify=TRUE, users can set the shape of significant points.
signal.col	a character, if amplify=TRUE, users can set the colour of significant points, if signal.col=NULL, then the colors of significant points will not be changed.
signal.line	a number, the width of the lines of significant SNPs cross the circle.
highlight	a vector, names of SNPs which need to be highlighted.
highlight.cex	a number or vector, the size of points for SNPs which need to be highlighted.
highlight.pch	a number or vector, the pch of points for SNPs which need to be highlighted.
highlight.col	a number or vector, the col of points for SNPs which need to be highlighted.
chr.labels	a vector, the labels for the chromosomes of density plot and circle-Manhattan plot.
chr.den.col	a character or vector or NULL, the colour for the SNP density. 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 around the circle. If chr.den.col=NULL, the density bar will not be attached on the bottom of manhattan plot.
cir.band	a number, the space between circles, the default is 1.
cir.chr	logical, a boundary that represents chromosomes will be plotted on the periphery of a circle, the default is TRUE.
cir.chr.h	a number, the width for the boundary, if cir.chr=FALSE, then this parameter will be useless.
cir.legend	logical, whether to add the legend of each circle.
cir.legend.cex	a number, the size of the number of legend.
cir.legend.col	a character, the color of the axis of legend.
LOG10	logical, whether to change the p-value into log10(p-value).
box	logical, this function draws a box around the current Manhattan plot.
conf.int	logical, whether to plot confidence interval on QQ-plot.
file.output	a logical, users can choose whether to output the plot results.
outpath	Only when file.output = TRUE, determines the path of the output file
file.type	a character, users can choose the different output formats of plot, so for, "jpg", "pdf", "tiff" can be selected by users.
dpi	a number, the picture resolution for .jpg and .tiff files. The default is 300.
height	the height of output files.
width	the width of output files.
memo	add a character to the output file name.
verbose	whether to print the reminder.

Value

Output files

Examples

data(pig60K, package = "rMVP")

MVP.Report(pig60K[,c(1:3, 5)], plot.type="m",
    threshold=0.05/nrow(pig60K), file.output=FALSE)

---

SNP Density

Description

SNP Density

Usage

MVP.Report.Density(
  Pmap,
  col = c("darkgreen", "yellow", "red"),
  dpi = 300,
  outpath = getwd(),
  memo = "MVP",
  bin.size = 1e+06,
  bin.max = NULL,
  file.type = "jpg",
  file.output = TRUE,
  verbose = TRUE
)

Arguments

Pmap	P value Map
col	The color vector
dpi	Number. Dots per inch for .jpg and .tiff files
outpath	Only when file.output = TRUE, determines the path of the output file
memo	Character. A text marker on output files
bin.size	the window size for counting SNP number
bin.max	maximum SNP number, for winows, which has more SNPs than bin.max, will be painted in same color
file.type	format of output figure
file.output	Whether to output the file
verbose	whether to print detail.

Value

Output file: <memo>.SNP_Density.<type>

Examples

data(pig60K, package = "rMVP")

MVP.Report.Density(pig60K, file.output=FALSE)

---

QQ Plot

Description

QQ Plot

Usage

MVP.Report.QQplot(
  P.values,
  taxa_name,
  col = c("blue"),
  cex = 0.5,
  threshold = NULL,
  amplify = TRUE,
  signal.col = "red",
  signal.pch = 19,
  signal.cex = 0.8,
  conf.int = TRUE,
  cex.axis = 1,
  conf.int.col = "grey",
  threshold.col = "red",
  outpath = getwd(),
  file.type = "jpg",
  memo = "MVP",
  box = TRUE,
  dpi = 300,
  file.output = TRUE,
  verbose = TRUE
)

Arguments

P.values	P values
taxa_name	The identifier of the phenotype will be used to generate a portion of the image file name. If the title parameter is NULL, it will also be part of the title.
col	default color is "blue"
cex	A numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. This starts as 1 when a device is opened, and is reset when the layout is changed, e.g. by setting mfrow. see par.
threshold	Number or Vector. The cutoff line on Manhattan plot, e.g. Bonfferoni correction. More than one significant line can be added onto one figure. If threshold=0 or NULL, the threshold line will not be added.
amplify	Logical value. If TRUE, the points that passed the threshold line will be highlighted
signal.col	Character. If "amplify" is TRUE, "signal.col" is used to set the color of significant points, if "signal.col" is NULL, the colors of significant points will not be changed
signal.pch	Number. If "amplify" is TRUE, users can set the type of significant points
signal.cex	Number. If "amplify" is TRUE, "signal.cex" is used to set the size of significant points
conf.int	Whether to draw a confidence interval
cex.axis	a number, controls the size of numbers of X-axis and the size of labels of circle plot.
conf.int.col	a character, the color of the confidence interval on QQ-plot.
threshold.col	Character or Vector. The colors of threshold lines
outpath	Only when file.output = TRUE, determines the path of the output file
file.type	A string or NULL is used to determine the type of output file. Can be "jpg", "pdf", "tiff". If it is NULL, it will use dev.new() to create a new graphics device in the current environment, which may be RStudioGD or the default device of the system.
memo	the prefix of the output image file.
box	A Boolean value that controls whether to draw a box around QQplot.
dpi	a number, the picture element for .jpg and .tiff files. The default is 300.
file.output	Logical value. If TRUE, the figures will be generated.
verbose	whether to print detail.

Value

Output file: <memo>.QQplot.<taxa_name>.<type>

Examples

data(pig60K, package = "rMVP")

MVP.Report(pig60K[1:10000,], plot.type="q", file.output=FALSE)

---

Print MVP Banner

Description

Build date: Aug 30, 2017 Last update: Dec 12, 2018

Usage

MVP.Version(width = 60, verbose = TRUE)

Arguments

width	the width of the message
verbose	whether to print detail.

Value

version number.

Author(s)

Lilin Yin, Haohao Zhang, and Xiaolei Liu

Examples

MVP.Version()

---

Genotyped by pig 60k chip

Description

This dataset gives the results of Genome-wide association study of 3 traits, individuals were genotyped by pig 60K chip.

Usage

data(pig60K)

Format

A dataframe containing 3 traits' Pvalue

---