synbreed (version 0.9-2)

create.gpData: Create genomic prediction data object

Description

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.

Usage

create.gpData(pheno = NULL, geno = NULL, map = NULL, pedigree = NULL,
              family = NULL, covar = NULL, reorderMap = TRUE, 
              map.unit = "cM", repeated  = NULL, modCovar = NULL)

Arguments

pheno

data.frame with individuals organized in rows and traits organized in columns. For unrepeated measures unique rownames should identify individuals. For repeated measures, the first column identifies individuals and a second col

geno

matrix with individuals organized in rows and markers organized in columns. Genotypes could be coded arbitrarily. Missing values should be coded as NA. Colums or rows with only missing values not allowed. Unique rownames

map

data.frame with one row for each marker and two columns (named chr and pos). First columns gives the chromosome (numeric or character but not factor) and second column the posit

pedigree

Object of class pedigree.

family

data.frame assigning individuals to families with names of individuals in rownames This information could be used for replacing of missing values with function codeGeno.

covar

data.frame with further covariates for all individuals that either appear in pheno, geno or pedigree$ID, e.g. sex or age. rownames must be specified to identify individuals. Typically this

reorderMap

logical. Should markers in geno and map be reordered by chromosome number and position within chromosome according to map (default = TRUE)?

map.unit

Character. Unit of position in map, i.e. 'cM' for genetic distance or 'bp' for physical distance (default = 'cM').

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 gpData. This argument is only required for repeated measurements.

modCovar

vector with colnames which identify columns with covariables in pheno. This argument is only required for repeated measurements.

Value

• Object of class gpData which is a list with the following elements
• covar
  data.frame with information on individuals
• pheno
  array (individuals x traits x replications) with phenotypic data
• geno
  matrix marker matrix containing genotypic data. Columns (marker) are in the same order as in map (if reorderMap=TRUE.)
• pedigree
  object of class pedigree
• map
  data.frame with columns 'chr' and 'pos' and markers sorted by 'pos' within 'chr'
• phenoCovars
  array with phenotypic covariates
• info
  list with additional information on data (coding of data, unit in map)

Details

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.

See Also

codeGeno, summary.gpData, gpData2data.frame

Examples

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)