IBD calculation for multi parental populations

IBD calculation for multi parental populations. Per cross IBD probabilities are calculated using calcIBD in the statgenIBD package. These probabilities are combined with optional phenotypic data and stored in a single object of class gDataMPP.

Usage

calcIBDMPP(
  crossNames,
  markerFiles,
  pheno,
  popType,
  mapFile,
  evalDist,
  grid = TRUE,
  verbose = FALSE
)

Arguments

crossNames

A character vector, the names of the crosses.

markerFiles

A character vector indicating the locations of the files with genotypic information for the populations. The files should be in tab-delimited format with a header containing marker names.

pheno

A data.frame or a list of data.frames with phenotypic data, with genotypes in the first column genotype and traits in the following columns. The trait columns should be numerical columns only. A list of data.frames can be used for replications, i.e. different trials.

popType

A character string indicating the type of population. One of DH, Fx, FxDH, BCx, BCxDH, BC1Sx, BC1SxDH, C3, C3DH, C3Sx, C3SxDH, C4, C4DH, C4Sx, C4SxDH (see Details).

mapFile

A character string indicating the location of the map file for the population. The file should be in tab-delimited format. It should consist of exactly three columns, marker, chromosome and position. There should be no header. The positions in the file should be in centimorgan.

evalDist

A numeric value, the maximum distance in cM between evaluation points.

grid

Should the extra markers that are added to assure the a maximum distince of evalDist be on a grid (TRUE) or in between marker existing marker positions (FALSE).

verbose

Should progress be printed?

Value

An object of class gDataMPP with the following components:

map

a data.frame containing map data. Map is sorted by chromosome and position.

markers

a 3D matrix containing IBD probabilities.

pheno

data.frame or list of data.frames containing phenotypic data.

kinship

a kinship matrix.

covar

a data.frame with extra covariates (including the name of the cross).

Details

IBD probabilities can be calculated for many different types of populations. In the following table all supported populations are listed. Note that the value of x in the population types is variable, with its maximum value depicted in the last column.

Population type	Cross	Description	max. x
DH	biparental	doubled haploid population
Fx	biparental	Fx population (F1, followed by x-1 generations of selfing)	8
FxDH	biparental	Fx, followed by DH generation	8
BCx	biparental	backcross, second parent is recurrent parent	9
BCxDH	biparental	BCx, followed by DH generation	9
BC1Sx	biparental	BC1, followed by x generations of selfing	7
BC1SxDH	biparental	BC1, followed by x generations of selfing and DH	6
C3	three-way	three way cross: (AxB) x C
C3DH	three-way	C3, followed by DH generation
C3Sx	three-way	C3, followed by x generations of selfing	7
C3SxDH	three-way	C3, followed by x generations of selfing and DH generation	6
C4	four-way	four-way cross: (AxB) x (CxD)
C4DH	four-way	C4, followed by DH generation
C4Sx	four-way	C4, followed by x generations of selfing	6
C4SxDH	four-way	C4, followed by x generations of selfing and DH generation	6

Examples

## Read phenotypic data.
pheno <- read.delim(system.file("extdata/multipop", "AxBxCpheno.txt",
                               package = "statgenMPP"))
## Rename first column to genotype.
colnames(pheno)[1] <- "genotype"


## Compute IBD probabilities for simulated population - AxB, AxC.
ABC <- calcIBDMPP(crossNames = c("AxB", "AxC"),
                  markerFiles = c(system.file("extdata/multipop", "AxB.txt",
                                              package = "statgenMPP"),
                                  system.file("extdata/multipop", "AxC.txt",
                                              package = "statgenMPP")),
                  pheno = pheno,
                  popType = "F4DH",
                  mapFile = system.file("extdata/multipop", "mapfile.txt",
                                        package = "statgenMPP"),
                  evalDist = 5)

summary(ABC)
#> map
#> 	Number of markers: 95 
#> 	Number of chromosomes: 5 
#> 
#> markers
#> 	Number of markers: 95 
#> 	Number of genotypes: 180 
#> 	Parents: A, B, C 
#> pheno
#> 	Number of traits: 1 
#> 	Traitnames: yield 
#> 	Number of genotypes: 180 
#> 
#> crosses          
#>  AxB:100  
#>  AxC: 80