Calculate the proportion of recurrent parent background (RPP) fully recovered in backcross progenies.

Usage

calc_rpp_bc(
  x,
  map_file,
  map_chr = "chr",
  map_pos = "pos",
  map_snp_ids = "snpid",
  rp = NULL,
  rp_num_code = 1,
  het_code = 0.5,
  na_code = NA,
  weighted = TRUE
)

Arguments

x: A numeric matrix of marker genotypes for backcross progenies and recurrent parent. Markers are columns and samples are rows.
map_file: A data frame of map file consisting of marker IDs and their chromosome numbers and positions as columns.
map_chr: A character value indicating the column name for chromosome IDs in map_file.
map_pos: A character value indicating the column name for chromosome positions in map file.
map_snp_ids: A character value indicating the column name for SNP IDs in map_file.
rp: An integer or character value indicating the row index or sample ID for the recurrent parent. if `NULL` the sample on the first row of x is used as the recurrent parent.
rp_num_code: A numeric value indicating the coding for the recurrent parent marker background in x.
het_code: A numeric value indicating the coding for heterozygous marker background in x.
na_code: A value indicating missing data in x.
weighted: A logical value indicating whether RPP values should be weighted or not.

Value

A data frame object comprising the RPP per chromosome and total RPP of sample IDs.

Details

The weighted RPP is computed as the sum of the product of relative distance weighting of markers and genotype scores that match the recurrent parent. Marker weights are obtained as half the normalized ratio of the physical distance between marker `i` and marker `i+1` to the total distance for all ordered markers on a chromosome.

The computation excludes heterozygous loci and only considers chromosome regions fully recovered as recurrent parent genetic background.

Examples

# \donttest{
# example code
library(panGenomeBreedr)
# Create a numeric matrix of genotype scores for 10 markers and 5 samples
num_dat <- matrix(c(1, 1, 0.5, 0.5, 1, 1, 1, 1, 1, 1, rep(0, 10),
                    1, 1, 0.5, 1, 1, 1, 1, 1, 0, 1,
                    1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
                    1, 1, 0, 1, 1, 1, 1, 1, 1, 0.5 ),
                  byrow = TRUE, ncol = 10)

rownames(num_dat) <- c('rp', 'dp', paste0('bc1_', 1:3))

colnames(num_dat) <- c(paste0('S1', '_', c(floor(seq(1000, 10000, len = 5)))),
                            paste0('S2', '_', c(floor(seq(1000, 10000, len = 5)))))

# Get map file by parsing SNP IDs
map_file <- parse_marker_ns(colnames(num_dat))

# Calculate weighted RPP
rpp <- calc_rpp_bc(x = num_dat,
                      map_file = map_file,
                      map_chr = 'chr',
                      map_pos = 'pos',
                      map_snp_ids = 'snpid',
                      rp = 1,
                      rp_num_code = 1,
                      weighted = TRUE)
# }