Calculate IBD probabilities — calcIBD • statgenIBD

Calculate IBD probabilities for different types of populations.

Usage

calcIBD(
  popType,
  markerFile,
  mapFile,
  evalPos = NULL,
  evalDist = NULL,
  grid = TRUE,
  verbose = FALSE
)

Arguments

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).
markerFile: A character string indicating the location of the file with genotypic information for the population. The file should be in tab-delimited format with a header containing marker names.
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.
evalPos: A data.frame with evaluation positions to which the calculations should be limited.
evalDist: An optional numerical value indicating the maximum distance for marker positions. Extra markers will be added based on the value of grid.
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 messages indicating the progress of the process be printed?

Value

An object of class IBDprob, a list with five elements,

map: a data.frame with chromosome and position of the markers.
markers: a 3-dimensional array of IBD probabilities with genotypes, markers and parents as array dimensions.
parents: the parents.
popType: the population type.

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

## Compute IBD probabilities for Steptoe Morex.
SxMIBD <- calcIBD(popType = "DH",
                  markerFile = system.file("extdata/SxM", "SxM_geno.txt",
                                        package = "statgenIBD"),
                  mapFile = system.file("extdata/SxM", "SxM_map.txt",
                                        package = "statgenIBD"))

## Check summary.
summary(SxMIBD)
#> population type:  DH 
#> Number of evaluation points:  116 
#> Number of individuals:  150 
#> Parents:  Morex Steptoe 

## Compute IBD probabilities for Steptoe Morex.
## Add extra evaluation positions in between exiting marker positions
## to assure evaluation positions are at most 5 cM apart.
SxMIBD_Ext <- calcIBD(popType = "DH",
                      markerFile = system.file("extdata/SxM", "SxM_geno.txt",
                                              package = "statgenIBD"),
                      mapFile = system.file("extdata/SxM", "SxM_map.txt",
                                            package = "statgenIBD"),
                      evalDist = 5)

## Check summary.
summary(SxMIBD_Ext)
#> population type:  DH 
#> Number of evaluation points:  226 
#> Number of individuals:  150 
#> Parents:  Morex Steptoe