Description Usage Arguments Details Value Note Author(s) See Also Examples
This function combines all raw data sources in a single, unified data object of class gpData
. This is a list
with elements for phenotypic, genotypic, marker map, pedigree and further covariate data. All elements are optional.
1 2 3 |
pheno |
|
geno |
|
map |
|
pedigree |
Object of class |
family |
|
covar |
|
reorderMap |
|
map.unit |
|
repeated |
This column is used to identify the replications of the phenotypic values. The unique values become the names of the third dimension of the pheno object in the |
modCovar |
|
na.string |
|
cores |
|
The class gpData
is designed to provide a unified framework for data related to genomic prediction analysis. Every data source can be omitted. In this case, the corresponding argument must be NULL
. By default (argument reorderMap
), markers in geno
are ordered by their position in map
. Individuals are ordered in alphabetical order.
An object of class gpData
can contain different subsets of individuals or markers in the elements pheno
, geno
and pedigree
. In this case the id
in covar
comprises all individuals that either appear in pheno
, geno
and pedigree
. Two additional columns in covar
named phenotyped
and genotyped
are automatically generated to identify individuals that appear in the corresponding gpData
object.
Object of class gpData
which is a list
with the following elements
covar |
|
pheno |
|
geno |
|
pedigree |
object of class |
map |
|
phenoCovars |
|
info |
|
In case of missing row names or column names in one item, information is substituted from other elements (assuming the same order of individuals/markers) and a warning specifying the assumptions is returned. Please check them carefully.
Valentin Wimmer and Hans-Juergen Auinger with contributions be Peter VandeHaar
codeGeno
, summary.gpData
, gpData2data.frame
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | set.seed(123)
# 9 plants with 2 traits
n <- 9 # only for n > 6
pheno <- data.frame(Yield = rnorm(n,200,5), Height=rnorm(n,100,1))
rownames(pheno) <- letters[1:n]
# marker matrix
geno <- matrix(sample(c("AA","AB","BB",NA),size=n*12,replace=TRUE,
prob=c(0.6,0.2,0.1,0.1)),nrow=n)
rownames(geno) <- letters[n:1]
colnames(geno) <- paste("M",1:12,sep="")
# genetic map
# one SNP is not mapped (M5) and will therefore be removed
map <- data.frame(chr=rep(1:3,each=4),pos=rep(1:12))
map <- map[-5,]
rownames(map) <- paste("M",c(1:4,6:12),sep="")
# simulate pedigree
ped <- simul.pedigree(3,c(3,3,n-6))
# combine in one object
gp <- create.gpData(pheno,geno,map,ped)
summary(gp)
# 9 plants with 2 traits , 3 replcations
n <- 9 #
pheno <- data.frame(ID = rep(letters[1:n],3), rep = rep(1:3,each=n),
Yield = rnorm(3*n,200,5), Height=rnorm(3*n,100,1))
# combine in one object
gp2 <- create.gpData(pheno,geno,map,repeated="rep")
summary(gp2)
|
Warning message:
In create.gpData(pheno, geno, map, ped) :
not all markers in 'geno' mapped in 'map'. gaps filled with 'NA'
object of class 'gpData'
covar
No. of individuals 9
phenotyped 0
genotyped 0
pheno
No. of traits: 2
Yield Height
Min. :193.7 Min. : 98.03
1st Qu.:197.2 1st Qu.: 99.55
Median :200.4 Median :100.36
Mean :200.7 Mean :100.16
3rd Qu.:202.3 3rd Qu.:100.50
Max. :208.6 Max. :101.79
geno
No. of markers 12
genotypes AA AB BB
frequencies 0.6203704 0.212963 0.09259259
NA's 7.407 %
map
No. of mapped markers 11
No. of chromosomes 3
markers per chromosome
1 2 3
4 3 4
pedigree
Number of
individuals 9
males : NA , females : NA
Par 1 (sire) 4
Par 2 (dam) 4
generations 3
Warning message:
In create.gpData(pheno, geno, map, repeated = "rep") :
not all markers in 'geno' mapped in 'map'. gaps filled with 'NA'
object of class 'gpData'
covar
No. of individuals 9
phenotyped 9
genotyped 9
pheno
No. of traits: 2
No. of repeated measures: 3
Yield Height
Min. :194.9 Min. : 98.33
1st Qu.:198.3 1st Qu.: 99.28
Median :200.9 Median : 99.93
Mean :201.0 Mean : 99.84
3rd Qu.:203.0 3rd Qu.:100.27
Max. :210.9 Max. :101.84
geno
No. of markers 12
genotypes AA AB BB
frequencies 0.6203704 0.212963 0.09259259
NA's 7.407 %
map
No. of mapped markers 11
No. of chromosomes 3
markers per chromosome
1 2 3
4 3 4
pedigree
NULL
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