ghap.ancmark {GHap}

Per marker ancestry proportions

Description

Given smoothed ancestry predictions obtained with the ghap.ancsmooth function, per marker ancestry proportions are calculated across selected individuals.

Usage

 ghap.ancmark(object, ancsmooth, ids)

Arguments

object

A GHap.phase object.

ancsmooth

A list containing smoothed ancestry classifications, such as supplied by the ghap.ancsmooth function.

ids

A character vector specifying which individuals to use for the calculations.

Details

This function takes smoothed ancestry classifications provided by the ghap.ancsmooth function and calculates, for each marker, the proportion of haplotypes carrying each ancestry label. The resulting output serve as a proxy for locus-specific ancestry proportions.

Value

The function returns a dataframes containing the following columns:

CHR

Chromosome name.

MARKER

Marker name.

BP

Marker position.

...

A number of columns (one for each ancestry label) giving the proportion of haplotypes carrying the respective ancestry label.

Author(s)

Yuri Tani Utsunomiya <[e-mail]>

See Also

ghap.ancsmooth

Examples

# #### DO NOT RUN IF NOT NECESSARY ###
# 
# # Copy phase data in the current working directory
# exfiles <- ghap.makefile(dataset = "example",
#                          format = "phase",
#                          verbose = TRUE)
# file.copy(from = exfiles, to = "./")
# 
# # Load phase data
# 
# phase <- ghap.loadphase("example")
# 
# # Calculate marker density
# mrkdist <- diff(phase$bp)
# mrkdist <- mrkdist[which(mrkdist > 0)]
# density <- mean(mrkdist)
# 
# # Generate blocks for admixture events up to g = 10 generations in the past
# # Assuming mean block size in Morgans of 1/(2*g)
# # Approximating 1 Morgan ~ 100 Mbp
# g <- 10
# window <- (100e+6)/(2*g)
# window <- ceiling(window/density)
# step <- ceiling(window/4)
# blocks <- ghap.blockgen(phase, windowsize = window,
#                         slide = step, unit = "marker")
# 
# # Supervised analysis
# train <- unique(phase$id[which(phase$pop != "Cross")])
# prototypes <- ghap.anctrain(object = phase, train = train,
#                             method = "supervised")
# hapadmix <- ghap.anctest(object = phase,
#                          blocks = blocks,
#                          prototypes = prototypes,
#                          test = unique(phase$id))
# anctracks <- ghap.ancsmooth(object = phase, admix = hapadmix)
# ghap.ancplot(ancsmooth = anctracks)
# 
# ### RUN ###
# 
# # Get per marker ancestry proportions for 'Pure1'
# pure1 <- unique(phase$id[which(phase$pop == "Pure1")])
# ancmark <- ghap.ancmark(object = phase,
#                         ancsmooth = anctracks,
#                         ids = pure1)
# 
# # Plot 'Pure2' introgression into 'Pure1'
# ghap.manhattan(data = ancmark, chr = "CHR",
#                bp = "BP", y = "Pure2", type = "h")