hscovar (version 0.4.2)

testdata: Description of the testdata

Description

The data set contains paternal haplotypes, maternal LD and genetic map positions that are required to calculate the covariance between pairs of markers.

The raw data can be downloaded at the source given below. Then, executing the following R code leads to the data that have been provided as testdata.RData.

H.sire

(2N x p) haplotype matrix for sires for all chromosomes (2 lines per sire)

matLD

(p x p) matrix of maternal LD between pairs of p markers; matrix is block diagonal in case of multiple chromosomes

pos.chr

list of vectors of genetic map positions per chromosome

Examples

# NOT RUN {
## data.frame of estimates of paternal recombination rate and maternal LD
load('Result.RData')
## list of haplotypes of sires for each chromosome
load('sire_haplotypes.RData')
## physical map
map <- read.table('map50K_ARS_reordered.txt', header = T)
## select target region
chr <- 1
window <- 301:600
## map information of target region
map.target <- map[map$Chr == chr, ][window, ]
Result.target <- Result[(Result$Chr == chr) & (Result$SNP1 %in% window) &
  (Result$SNP2 %in% window), ]
## SNP position in Morgan approximated from recombination rate
part <- Result.target[Result.target$SNP1 == window[1], ]
sp <- smooth.spline(x = map.target$locus_Mb[part$SNP2 - window[1] + 1], y = part$Theta, df = 4)
pos.snp <- predict(sp, x =  map.target$locus_Mb[window - window[1] + 1])$y
## list of SNPs positions
pos.chr <- list(pos.snp)
## haplotypes of sires (mating candidates) in target region
H.sire <- rlist::list.rbind(haps[[chr]])[, window]
## matrix of maternal LD (block diagonal if multiple chromosome)
matLD <- matrix(0, ncol = length(window), nrow = length(window))
## off-diagonal elements
for(l in 1:nrow(Result.target)){
  id1 <- Result.target$SNP1[l] - window[1] + 1
  id2 <- Result.target$SNP2[l] - window[1] + 1
  matLD[id1, id2] <- matLD[id2, id1] <- Result.target$D[l]
}
## diagonal elements
for(k in unique(Result.target$SNP1)){
  id <- k - window[1] + 1
  p <- Result.target$fAA[Result.target$SNP1 == k] + Result.target$fAB[Result.target$SNP1 == k]
  matLD[id, id] <- max(p * (1 - p))
}
# }