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Type: Package
Title: QTL Genome-Wide Composite Interval Mapping
Version: 3.4
Date: 2022-02-24
Author: Zhou Ya-Hui, Zhang Ya-Wen, Wen Yang-Jun, Wang Shi-Bo, and Zhang Yuan-Ming
Maintainer: Yuanming Zhang<soyzhang@mail.hzau.edu.cn>
Description: Conduct multiple quantitative trait loci (QTL) and QTL-by-environment interaction (QEI) mapping via ordinary or compressed variance component mixed models with random- or fixed QTL/QEI effects. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve or on each locus curve are viewed as potential main-effect QTLs and QEIs, all their effects are included in a multi-locus model, their effects are estimated by both least angle regression and empirical Bayes (or adaptive lasso) in backcross and F2 populations, and true QTLs and QEIs are identified by likelihood radio test. See Zhou et al. (2022) <doi:10.1093/bib/bbab596> and Wen et al. (2018) <doi:10.1093/bib/bby058>.
Encoding: UTF-8
Depends: R (≥ 3.5.0)
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Imports: Rcpp (≥ 0.12.17),methods,openxlsx,readxl,lars,stringr,data.table,glmnet,doParallel,foreach,MASS,qtl
LinkingTo: Rcpp
NeedsCompilation: yes
RoxygenNote: 7.1.2
Packaged: 2022-02-24 12:55:13 UTC; win10
Repository: CRAN
Date/Publication: 2022-02-24 18:30:02 UTC

DH example data

Description

GCIM format of DH dataset.

Usage

  data(DHdata)

Details

Input file for WangF function.

Author(s)

Maintainer: Yuanming Zhang<soyzhang@mail.hzau.edu.cn>

Process raw data

Description

Process raw data

Usage

Dodata(
  fileFormat = NULL,
  Population = NULL,
  method = NULL,
  Model = NULL,
  readraw = NULL,
  MultiEnv = FALSE
)

Arguments

fileFormat

Format of dataset.

Population

Population type.

method

Method "GCIM" or method "GCIM-QEI"

Model

Random or fixed model.

readraw

Raw data.

MultiEnv

Whether to perform multi-environment analysis

Value

a list

Examples

data(F2data)
readraw<-Readdata(file=F2data,fileFormat="GCIM",
method="GCIM-QEI",filecov=NULL,
MCIMmap=NULL,MultiEnv=TRUE)
doda<-Dodata(fileFormat="GCIM",Population="F2",
method="GCIM-QEI",Model="Random",
readraw,MultiEnv=TRUE)

F2 example data from 2 environments

Description

GCIM format of F2 dataset whith GCIM-QEI method.

Usage

  data(F2data)

Details

Input file for ZhouF function.

Author(s)

Maintainer: Yuanming Zhang<soyzhang@mail.hzau.edu.cn>

QTL Genome-Wide Composite Interval Mapping

Description

QTL Genome-Wide Composite Interval Mapping

Usage

QTL.gCIMapping(
  file = NULL,
  fileFormat = "GCIM",
  filecov = NULL,
  MCIMmap = NULL,
  Population = NULL,
  method = "GCIM-QEI",
  MultiEnv = FALSE,
  Model = "Random",
  WalkSpeed = 1,
  CriLOD = 3,
  CriDis = 5,
  Likelihood = "REML",
  SetSeed = 11001,
  flagrqtl = FALSE,
  DrawPlot = TRUE,
  PlotFormat = "jpeg",
  Resolution = "Low",
  Trait = NULL,
  dir = NULL,
  CLO = NULL
)

Arguments

file

File path and name in your computer.

fileFormat

Format for input file: GCIM, ICIM, Cart, or MCIM.

filecov

Covariate file of QTLIciMapping or QTLNetwork.

MCIMmap

Map file of QTLNetwork.

Population

Population type: F2, BC1, BC2, DH, RIL.

method

Method "GCIM" or method "GCIM-QEI".

MultiEnv

Whether to perform multi-environment analysis.

Model

Random or fixed model.

WalkSpeed

Walk speed for Genome-wide Scanning.

CriLOD

Critical LOD scores for significant QTL.

CriDis

The distance of optimization.

Likelihood

This parameter is only for F2 population,including REML (restricted maximum likelihood ) and ML(maximum likelihood).

SetSeed

In which the cross validation experiment is needed. Generally speaking, the random seed in the cross-validation experiment was set as 11001. If some known genes are not identified by the seed, users may try to use some new random seeds. At this case, one better result may be obtained.

flagrqtl

This parameter is only for F2 population, flagrqtl="FALSE" in the first running. If the other software detects only one QTL in a neighborhood but this software finds two linked QTLs (one with additive effect and another with dominant effect) in the region, let flagrqtl="TRUE"

DrawPlot

This parameter is for all the populations, including FALSE and TRUE, DrawPlot=FALSE indicates no figure output, DrawPlot=TRUE indicates the output of the figure against genome position.

PlotFormat

This parameter is for all the figure files, including *.jpeg, *.png, *.tiff and *.pdf.

Resolution

This parameter is for all the figure files, including Low and High.

Trait

Trait=1:3 indicates the analysis from the first trait to the third trait.

dir

This parameter is for the save path.

CLO

Number of CPUs.

Examples

data(F2data)
QTL.gCIMapping(file=F2data,Population="F2",
MultiEnv=TRUE,Model="Random",CriLOD=3,
Trait=1,dir=tempdir(),CLO=2)

Read raw data

Description

Read raw data

Usage

Readdata(
  file = NULL,
  fileFormat = NULL,
  method = NULL,
  filecov = NULL,
  MCIMmap = NULL,
  MultiEnv = FALSE
)

Arguments

file

Dataset input

fileFormat

Format of dataset.

method

Method "GCIM" or method "GCIM-QEI"

filecov

Covariate file of QTLIciMapping or QTLNetwork.

MCIMmap

Map file of QTLNetwork.

MultiEnv

Whether to perform multi-environment analysis

Value

a list

Examples

data(F2data)
Readdata(file=F2data,fileFormat="GCIM",
method="GCIM-QEI",filecov=NULL,
MCIMmap=NULL,MultiEnv=TRUE)

To perform QTL mapping with wang method

Description

To perform QTL mapping with wang method

Usage

WangF(
  pheRaw = NULL,
  genRaw = NULL,
  mapRaw1 = NULL,
  yygg1 = NULL,
  flagRIL = NULL,
  cov_en = NULL,
  Population = NULL,
  WalkSpeed = NULL,
  CriLOD = NULL
)

Arguments

pheRaw

phenotype matrix.

genRaw

genotype matrix.

mapRaw1

linkage map matrix.

yygg1

the transformed covariate matrix.

flagRIL

if RIL or not.

cov_en

raw covariate matrix.

Population

population flag.

WalkSpeed

Walk speed for Genome-wide Scanning.

CriLOD

Critical LOD scores for significant QTL.

Value

a list

Examples

data(DHdata)
readraw<-Readdata(file=DHdata,fileFormat="GCIM",
method="GCIM",filecov=NULL,MCIMmap=NULL,MultiEnv=FALSE)
DoResult<-Dodata(fileFormat="GCIM",Population="DH",
method="GCIM",Model="Random",readraw,MultiEnv=FALSE)
ws<-WangF(pheRaw=DoResult$pheRaw,genRaw=DoResult$genRaw,
mapRaw1=DoResult$mapRaw1,yygg1=DoResult$yygg1,
flagRIL=DoResult$flagRIL,cov_en=DoResult$cov_en,
Population="DH",WalkSpeed=1,CriLOD=2.5)

The second step of wang method

Description

The second step of wang method

Usage

WangS(
  flag = NULL,
  CriLOD = NULL,
  NUM = NULL,
  pheRaw = NULL,
  chrRaw_name = NULL,
  yygg = NULL,
  mx = NULL,
  phe = NULL,
  chr_name = NULL,
  gen = NULL,
  mapname = NULL,
  CLO = NULL
)

Arguments

flag

fix or random model.

CriLOD

Critical LOD scores for significant QTL.

NUM

The number of trait.

pheRaw

Raw phenotype matrix.

chrRaw_name

raw chromosome name.

yygg

covariate matrix.

mx

raw genotype matrix.

phe

phenotype matrix.

chr_name

chromosome name.

gen

genotype matrix.

mapname

linkage map matrix.

CLO

Number of CPUs.

Value

a list

Examples

data(DHdata)
readraw<-Readdata(file=DHdata,fileFormat="GCIM",
method="GCIM",filecov=NULL,MCIMmap=NULL,MultiEnv=FALSE)
DoResult<-Dodata(fileFormat="GCIM",Population="DH",
method="GCIM",Model="Random",readraw,MultiEnv=FALSE)
W1re<-WangF(pheRaw=DoResult$pheRaw,genRaw=DoResult$genRaw,
mapRaw1=DoResult$mapRaw1,yygg1=DoResult$yygg1,
flagRIL=DoResult$flagRIL,cov_en=DoResult$cov_en,
Population="DH",WalkSpeed=1,CriLOD=2.5)
ws<-WangS(flag=DoResult$flag,CriLOD=2.5,NUM=1,
pheRaw=DoResult$pheRaw,chrRaw_name=W1re$chrRaw_name,
yygg=W1re$yygg,mx=W1re$mx,phe=W1re$phe,
chr_name=W1re$chr_name,gen=W1re$gen,
mapname=W1re$mapname,CLO=1)

To perform QTL mapping with Wen method

Description

To perform QTL mapping with Wen method

Usage

WenF(
  pheRaw = NULL,
  genRaw = NULL,
  mapRaw1 = NULL,
  yygg1 = NULL,
  cov_en = NULL,
  WalkSpeed = NULL,
  CriLOD = NULL,
  dir = NULL
)

Arguments

pheRaw

phenotype matrix.

genRaw

genotype matrix.

mapRaw1

linkage map matrix.

yygg1

the transformed covariate matrix.

cov_en

raw covariate matrix.

WalkSpeed

Walk speed for Genome-wide Scanning.

CriLOD

Critical LOD scores for significant QTL.

dir

file path in your computer.

Value

a list

Examples

data(F2data)
readraw<-Readdata(file=F2data,fileFormat="GCIM",
method="GCIM",filecov=NULL,MCIMmap=NULL,
MultiEnv=FALSE)
DoResult<-Dodata(fileFormat="GCIM",Population="F2",
method="GCIM",Model="Random",readraw,MultiEnv=FALSE)
wf<-WenF(pheRaw=DoResult$pheRaw,
genRaw=DoResult$genRaw,mapRaw1=DoResult$mapRaw1,
yygg1=DoResult$yygg1,cov_en=DoResult$cov_en,
WalkSpeed=1,CriLOD=2.5,dir=tempdir())

The second step of Wen method

Description

The second step of Wen method

Usage

WenS(
  flag = NULL,
  CriLOD = NULL,
  NUM = NULL,
  pheRaw = NULL,
  Likelihood = NULL,
  SetSeed = NULL,
  flagrqtl = NULL,
  yygg = NULL,
  mx = NULL,
  phe = NULL,
  chr_name = NULL,
  v.map = NULL,
  gen.raw = NULL,
  a.gen.orig = NULL,
  d.gen.orig = NULL,
  n = NULL,
  names.insert2 = NULL,
  X.ad.tran.data = NULL,
  X.ad.t4 = NULL,
  dir = NULL
)

Arguments

flag

random or fix model.

CriLOD

LOD score.

NUM

the number of trait.

pheRaw

raw phenotype matrix.

Likelihood

likelihood function.

SetSeed

random seed set in which,the cross validation is needed.

flagrqtl

do CIM or not.

yygg

covariate matrix.

mx

raw genotype matrix.

phe

phenotype matrix.

chr_name

chromosome name.

v.map

linkage map matrix.

gen.raw

raw genotype matrix.

a.gen.orig

additive genotype matrix.

d.gen.orig

dominant genotype matrix.

n

number of individual.

names.insert2

linkage map after insert.

X.ad.tran.data

genotype matrix after insert.

X.ad.t4

genotype matrix.

dir

file storage path.

Value

a list

Examples

data(F2data)
readraw<-Readdata(file=F2data,fileFormat="GCIM",
method="GCIM",filecov=NULL,MCIMmap=NULL,MultiEnv=FALSE)
DoResult<-Dodata(fileFormat="GCIM",Population="F2",
method="GCIM",Model="Random",readraw,MultiEnv=FALSE)
WEN1re<-WenF(pheRaw=DoResult$pheRaw,
genRaw=DoResult$genRaw,mapRaw1=DoResult$mapRaw1,
yygg1=DoResult$yygg1,cov_en=DoResult$cov_en,
WalkSpeed=1,CriLOD=2.5,dir=tempdir())
ws<-WenS(flag=DoResult$flag,CriLOD=2.5,NUM=1,
pheRaw=DoResult$pheRaw,Likelihood="REML",
SetSeed=11001,flagrqtl=FALSE,
yygg=WEN1re$yygg,mx=WEN1re$mx,phe=WEN1re$phe,
chr_name=WEN1re$chr_name,v.map=WEN1re$v.map,
gen.raw=WEN1re$gen.raw,
a.gen.orig=WEN1re$a.gen.orig,
d.gen.orig=WEN1re$d.gen.orig,n=WEN1re$n,
names.insert2=WEN1re$names.insert2,
X.ad.tran.data=WEN1re$X.ad.tran.data,
X.ad.t4=WEN1re$X.ad.t4,dir=tempdir())

To perform QTL mapping with Wen method

Description

To perform QTL mapping with Wen method

Usage

ZhouF(
  pheRaw = NULL,
  genRaw = NULL,
  mapRaw1 = NULL,
  WalkSpeed = NULL,
  CriLOD = NULL,
  dir = NULL
)

Arguments

pheRaw

phenotype matrix.

genRaw

genotype matrix.

mapRaw1

linkage map matrix.

WalkSpeed

Walk speed for Genome-wide Scanning.

CriLOD

Critical LOD scores for significant QTL.

dir

file path in your computer.

Value

a list

Examples

data(F2data)
readraw<-Readdata(file=F2data,fileFormat="GCIM",
method="GCIM-QEI",filecov=NULL,
MCIMmap=NULL,MultiEnv=TRUE)
DoResult<-Dodata(fileFormat="GCIM",
Population="F2",method="GCIM-QEI",
Model="Random",readraw,MultiEnv=TRUE)
ZhouMatrices<-ZhouF(pheRaw=DoResult$pheRaw,
genRaw=DoResult$genRaw,
mapRaw1=DoResult$mapRaw1,
WalkSpeed=1,CriLOD=3,
dir=tempdir())

The second step of Zhou method for multiple environments

Description

The second step of Zhou method for multiple environments

Usage

ZhouMethod(
  Model = NULL,
  pheRaw = NULL,
  genRaw = NULL,
  mapRaw = NULL,
  CriLOD = NULL,
  NUM = NULL,
  EnvNum = NULL,
  yygg = NULL,
  genoname = NULL,
  Ax0 = NULL,
  Hx0 = NULL,
  Bx0 = NULL,
  Ax = NULL,
  Hx = NULL,
  Bx = NULL,
  dir = NULL,
  CriDis = NULL,
  CLO = NULL
)

Arguments

Model

Random or fixed model.

pheRaw

phenotype matrix.

genRaw

genotype matrix.

mapRaw

linkage map matrix.

CriLOD

Critical LOD scores for significant QTL.

NUM

The serial number of the trait to be analyzed.

EnvNum

The number of environments for each trait is a vector.

yygg

covariate matrix.

genoname

linkage map matrix with pseudo markers inserted.

Ax0

AA genotype matrix.

Hx0

Aa genotype matrix.

Bx0

aa genotype matrix.

Ax

AA genotype matrix with pseudo markers inserted.

Hx

Aa genotype matrix with pseudo markers inserted.

Bx

aa genotype matrix with pseudo markers inserted.

dir

file storage path.

CriDis

The distance of optimization.

CLO

Number of CPUs.

Value

a list

Examples

data(F2data)
readraw<-Readdata(file=F2data,fileFormat="GCIM",
method="GCIM-QEI",filecov=NULL,
MCIMmap=NULL,MultiEnv=TRUE)
DoResult<-Dodata(fileFormat="GCIM",
Population="F2",method="GCIM-QEI",
Model="Random",readraw,MultiEnv=TRUE)
ZhouMatrices<-ZhouF(pheRaw=DoResult$pheRaw,
genRaw=DoResult$genRaw,mapRaw1=DoResult$mapRaw1,
WalkSpeed=1,CriLOD=3,dir=tempdir())
OutputZhou<-ZhouMethod(Model="Random",
pheRaw=DoResult$pheRaw,genRaw=DoResult$genRaw,
mapRaw=ZhouMatrices$mapRaw,CriLOD=3,NUM=1,
EnvNum=DoResult$EnvNum,yygg=DoResult$yygg1,
genoname=ZhouMatrices$genoname,
Ax0=ZhouMatrices$Ax0,Hx0=ZhouMatrices$Hx0,
Bx0=ZhouMatrices$Bx0,Ax=ZhouMatrices$Ax,
Hx=ZhouMatrices$Hx,Bx=ZhouMatrices$Bx,
dir=tempdir(),CriDis=5,CLO=2)

The second step of Zhou method for single environment

Description

The second step of Zhou method for single environment

Usage

ZhouMethod_single_env(
  Model = NULL,
  pheRaw = NULL,
  genRaw = NULL,
  mapRaw = NULL,
  CriLOD = NULL,
  NUM = NULL,
  yygg = NULL,
  genoname = NULL,
  Ax0 = NULL,
  Hx0 = NULL,
  Bx0 = NULL,
  Ax = NULL,
  Hx = NULL,
  Bx = NULL,
  dir = NULL,
  CriDis = NULL,
  CLO = NULL
)

Arguments

Model

Random or fixed model.

pheRaw

phenotype matrix.

genRaw

genotype matrix.

mapRaw

linkage map matrix.

CriLOD

Critical LOD scores for significant QTL.

NUM

The serial number of the trait to be analyzed.

yygg

covariate matrix.

genoname

linkage map matrix with pseudo markers inserted.

Ax0

AA genotype matrix.

Hx0

Aa genotype matrix.

Bx0

aa genotype matrix.

Ax

AA genotype matrix with pseudo markers inserted.

Hx

Aa genotype matrix with pseudo markers inserted.

Bx

aa genotype matrix with pseudo markers inserted.

dir

file storage path.

CriDis

The distance of optimization.

CLO

Number of CPUs.

Value

a list

Examples

data(F2data)
readraw<-Readdata(file=F2data,fileFormat="GCIM",
method="GCIM-QEI",filecov=NULL,
MCIMmap=NULL,MultiEnv=FALSE)
DoResult<-Dodata(fileFormat="GCIM",Population="F2",
method="GCIM-QEI",Model="Random",
readraw,MultiEnv=FALSE)
ZhouMatrices<-ZhouF(pheRaw=DoResult$pheRaw,
genRaw=DoResult$genRaw,mapRaw1=DoResult$mapRaw1,
WalkSpeed=1,CriLOD=3,dir=tempdir())
OutputZhou<-ZhouMethod_single_env(Model="Random",
pheRaw=DoResult$pheRaw,genRaw=DoResult$genRaw,
mapRaw=ZhouMatrices$mapRaw,CriLOD=3,NUM=1,
yygg=DoResult$yygg1,genoname=ZhouMatrices$genoname,
Ax0=ZhouMatrices$Ax0,Hx0=ZhouMatrices$Hx0,
Bx0=ZhouMatrices$Bx0,Ax=ZhouMatrices$Ax,
Hx=ZhouMatrices$Hx,Bx=ZhouMatrices$Bx,
dir=tempdir(),CriDis=5,CLO=2)

To insert marker in genotype.

Description

a method that can insert marker in genotype.

Usage

markerinsert(mp,geno,map,cl,gg1,gg2,gg0,flagRIL)

Arguments

mp

linkage map matrix after insert.

geno

genotype matrix.

map

linkage map matrix.

cl

walk speed.

gg1

raw covariate matrix.

gg2

code for type 1.

gg0

code for missing.

flagRIL

RIL population or not.

Author(s)

Zhang Ya-Wen, Wen Yang-Jun, Wang Shi-Bo, and Zhang Yuan-Ming
Maintainer: Yuanming Zhang<soyzhang@mail.hzau.edu.cn>

Examples

## Not run: 
mp=matrix(c(197.9196,198.7536,199.5876,200.4216,201.2453,
202.0691,202.8928,203.7521,204.6113,205.4706,206.3298,207.1891,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,3,3,3,3,3,3,
1,1,1,2,2,2,3,3,3,3,3,3,1,2,3,4,5,6,7,8,9,10,11,12),12,5)
map=matrix(c(1,1,1,1,197.9196,200.4216,202.8928,207.1891),4,2)
geno=matrix(c(1,99,99,99),1,4)
QTL.gCIMapping::markerinsert(mp,geno,map,cl=1,gg1=1,gg2=-1,
gg0=99,flagRIL=1)

## End(Not run)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.