| Title: | QTL Genome-Wide Composite Interval Mapping |
|---|---|
| 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>. |
| Authors: | Zhou Ya-Hui, Zhang Ya-Wen, Wen Yang-Jun, Wang Shi-Bo, and Zhang Yuan-Ming |
| Maintainer: | Yuanming Zhang<[email protected]> |
| License: | GPL (>= 2) |
| Version: | 3.4 |
| Built: | 2024-11-20 02:48:48 UTC |
| Source: | https://github.com/cran/QTL.gCIMapping |
GCIM format of DH dataset.
data(DHdata)data(DHdata)
Input file for WangF function.
Maintainer: Yuanming Zhang<[email protected]>
Process raw data
Dodata( fileFormat = NULL, Population = NULL, method = NULL, Model = NULL, readraw = NULL, MultiEnv = FALSE )Dodata( fileFormat = NULL, Population = NULL, method = NULL, Model = NULL, readraw = NULL, MultiEnv = FALSE )
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 |
a list
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)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)
GCIM format of F2 dataset whith GCIM-QEI method.
data(F2data)data(F2data)
Input file for ZhouF function.
Maintainer: Yuanming Zhang<[email protected]>
a method that can insert marker in genotype.
markerinsert(mp,geno,map,cl,gg1,gg2,gg0,flagRIL)markerinsert(mp,geno,map,cl,gg1,gg2,gg0,flagRIL)
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. |
Zhang Ya-Wen, Wen Yang-Jun, Wang Shi-Bo, and Zhang Yuan-Ming
Maintainer: Yuanming Zhang<[email protected]>
## 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)## 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)
QTL Genome-Wide Composite Interval Mapping
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 )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 )
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. |
data(F2data) QTL.gCIMapping(file=F2data,Population="F2", MultiEnv=TRUE,Model="Random",CriLOD=3, Trait=1,dir=tempdir(),CLO=2)data(F2data) QTL.gCIMapping(file=F2data,Population="F2", MultiEnv=TRUE,Model="Random",CriLOD=3, Trait=1,dir=tempdir(),CLO=2)
Read raw data
Readdata( file = NULL, fileFormat = NULL, method = NULL, filecov = NULL, MCIMmap = NULL, MultiEnv = FALSE )Readdata( file = NULL, fileFormat = NULL, method = NULL, filecov = NULL, MCIMmap = NULL, MultiEnv = FALSE )
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 |
a list
data(F2data) Readdata(file=F2data,fileFormat="GCIM", method="GCIM-QEI",filecov=NULL, MCIMmap=NULL,MultiEnv=TRUE)data(F2data) Readdata(file=F2data,fileFormat="GCIM", method="GCIM-QEI",filecov=NULL, MCIMmap=NULL,MultiEnv=TRUE)
To perform QTL mapping with wang method
WangF( pheRaw = NULL, genRaw = NULL, mapRaw1 = NULL, yygg1 = NULL, flagRIL = NULL, cov_en = NULL, Population = NULL, WalkSpeed = NULL, CriLOD = NULL )WangF( pheRaw = NULL, genRaw = NULL, mapRaw1 = NULL, yygg1 = NULL, flagRIL = NULL, cov_en = NULL, Population = NULL, WalkSpeed = NULL, CriLOD = NULL )
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. |
a list
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)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
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 )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 )
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. |
a list
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$mapname,CLO=1)data(DHdata) readraw<-Readdata(file=DHdata,fileFormat="GCIM", method="GCIM",filecov=NULL,MCIMmap=NULL,MultiEnv=FALSE) DoResult<-Dodata(fileFormat="GCIM",Population="DH", method="GCIM",Model="Random",readraw,MultiEnv=FALSE) W1re<-WangF(pheRaw=DoResult$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
WenF( pheRaw = NULL, genRaw = NULL, mapRaw1 = NULL, yygg1 = NULL, cov_en = NULL, WalkSpeed = NULL, CriLOD = NULL, dir = NULL )WenF( pheRaw = NULL, genRaw = NULL, mapRaw1 = NULL, yygg1 = NULL, cov_en = NULL, WalkSpeed = NULL, CriLOD = NULL, dir = NULL )
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. |
a list
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$cov_en, WalkSpeed=1,CriLOD=2.5,dir=tempdir())data(F2data) readraw<-Readdata(file=F2data,fileFormat="GCIM", method="GCIM",filecov=NULL,MCIMmap=NULL, MultiEnv=FALSE) DoResult<-Dodata(fileFormat="GCIM",Population="F2", method="GCIM",Model="Random",readraw,MultiEnv=FALSE) wf<-WenF(pheRaw=DoResult$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
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 )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 )
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. |
a list
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$X.ad.t4,dir=tempdir())data(F2data) readraw<-Readdata(file=F2data,fileFormat="GCIM", method="GCIM",filecov=NULL,MCIMmap=NULL,MultiEnv=FALSE) DoResult<-Dodata(fileFormat="GCIM",Population="F2", method="GCIM",Model="Random",readraw,MultiEnv=FALSE) WEN1re<-WenF(pheRaw=DoResult$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
ZhouF( pheRaw = NULL, genRaw = NULL, mapRaw1 = NULL, WalkSpeed = NULL, CriLOD = NULL, dir = NULL )ZhouF( pheRaw = NULL, genRaw = NULL, mapRaw1 = NULL, WalkSpeed = NULL, CriLOD = NULL, dir = NULL )
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. |
a list
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$mapRaw1, WalkSpeed=1,CriLOD=3, dir=tempdir())data(F2data) readraw<-Readdata(file=F2data,fileFormat="GCIM", method="GCIM-QEI",filecov=NULL, MCIMmap=NULL,MultiEnv=TRUE) DoResult<-Dodata(fileFormat="GCIM", Population="F2",method="GCIM-QEI", Model="Random",readraw,MultiEnv=TRUE) ZhouMatrices<-ZhouF(pheRaw=DoResult$pheRaw, genRaw=DoResult$genRaw, mapRaw1=DoResult$mapRaw1, WalkSpeed=1,CriLOD=3, dir=tempdir())
The second step of Zhou method for multiple environments
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 )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 )
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. |
a list
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$Bx, dir=tempdir(),CriDis=5,CLO=2)data(F2data) readraw<-Readdata(file=F2data,fileFormat="GCIM", method="GCIM-QEI",filecov=NULL, MCIMmap=NULL,MultiEnv=TRUE) DoResult<-Dodata(fileFormat="GCIM", Population="F2",method="GCIM-QEI", Model="Random",readraw,MultiEnv=TRUE) ZhouMatrices<-ZhouF(pheRaw=DoResult$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
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 )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 )
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. |
a list
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)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)