R/simulateGenotypes.R
In susjoh/simperSNP: Simulates SNP Datasets For Simple And Complex Pedigrees With Recombination.
#' Simulate recombination events in R
#'
#' This function simulates genotypes within a pedigree, based on centiMorgan map
#' distances. Outputs genotypes as a GenABEL gwaa object.
#' @param ped data.frame of pedigree in "simple" format (Three columns for
#' ANIMAL, MOTHER and FATHER) or in "plink" format (Five to Six columns for
#' FAMILY, ANIMAL, FATHER, MOTHER, SEX and Phenotype, where the phenotype
#' column is optional). Is formatted within this function using
#' pedigree.format()
#' @param pedigree.type Character. "simple" or "plink"
#' @param cM Numeric vector of sex-averaged map distances in centiMorgans. Not
#' required if sex specific map distances are defined. First position should
#' be given as 0cM
#' @param cM.male Numeric vector of Male map distances in centiMorgans
#' @param cM.female Numeric vector of Female map distances in centiMorgans
#' @param founder.mafs Numeric vector of Minor allele frequencies for each
#' marker
#' @param chromosome.ids Vector of chromosome or linkage group IDs for each
#' marker
#' @param snp.names Optional character vector of SNP names
#' @param map.distances Optional vector of map distances in base pairs for
#' GenABEL object. Not used in the simulation.
#' @param error.rate numeric. Default is 1e-4. Future versions will allow vector.
#' @param missing.rate numeric. Default is 0.001. Future versions will allow vector.
#' @param xover.min.cM numeric. Minimum cM distance between two crossovers
#' @param xover.min.cM.male numeric. Minimum cM distance between two crossovers in males
#' @param xover.min.cM.female numeric. Minimum cM distance between two crossovers in females
simulateGenos <- function(ped,
pedigree.type = "simple",
cM = NULL,
cM.male = NULL,
cM.female = NULL,
founder.mafs = NULL,
chromosome.ids = NULL,
snp.names = NULL,
map.distances = NULL,
error.rate = 1e-4,
missing.rate = 0.001,
xover.min.cM = NULL,
xover.min.cM.male = NULL,
xover.min.cM.female = NULL){
require(plyr)
require(reshape2)
require(GenABEL)
require(kinship2)
phased.output <- TRUE
#~~ Format the pedigree
ped <- pedigree.format(ped, pedigree.type = pedigree.type)
#~~ melt pedfile to get a unique row for each gamete transfer
transped <- melt(ped[,c("ANIMAL", "MOTHER", "FATHER")], id.vars = "ANIMAL")
transped$variable <- as.character(transped$variable)
#~~ assign pedfile IDs to cohort and merge with transped
cohorts <- data.frame(ANIMAL = ped[,1],
Cohort = kindepth(ped[,1], ped[,3], ped[,2]))
suppressMessages(transped <- join(transped, cohorts))
#~~ Redefine columns
names(transped) <- c("Offspring.ID", "Parent.ID.SEX", "Parent.ID", "Cohort")
transped$Key <- paste(transped$Parent.ID, transped$Offspring.ID, sep = "_")
#~~ Recode founder gametes to 0 e.g. if one parent is unknown
transped$Cohort[which(transped$Parent.ID == 0)] <- 0
#~~ Check and format the recombination units
if( is.null(cM) & is.null(cM.male) & is.null(cM.female)) stop ("No recombination rates specified.")
if(!is.null(cM) & is.null(cM.male) & !is.null(cM.female)) warning("Recombination rate only defined in one sex. Missing sex defaults to r.")
if(!is.null(cM) & !is.null(cM.male) & is.null(cM.female)) warning("Recombination rate only defined in one sex. Missing sex defaults to r.")
if( is.null(cM) & is.null(cM.male) & !is.null(cM.female)) stop ("Recombination rate only defined in one sex. Use r= ... ")
if( is.null(cM) & !is.null(cM.male) & is.null(cM.female)) stop ("Recombination rate only defined in one sex. Use r= ...")
if(!is.null(cM) & is.null(cM.male) & is.null(cM.female)) message("Assuming that recombination rates are equal in both sexes.")
#~~ Use cM if no sex specific information given
if(is.null(cM.male )) cM.male <- cM
if(is.null(cM.female)) cM.female <- cM
#~~ Tidy up crossover interference parameters
if( is.null(xover.min.cM) & is.null(xover.min.cM.male) & is.null(xover.min.cM.female)) message ("Assuming no crossover interference.")
if(!is.null(xover.min.cM) & is.null(xover.min.cM.male) & !is.null(xover.min.cM.female)) warning("Crossover interference parameter only defined in one sex. Missing sex defaults to xover.min.cM.")
if(!is.null(xover.min.cM) & !is.null(xover.min.cM.male) & is.null(xover.min.cM.female)) warning("Crossover interference parameter only defined in one sex. Missing sex defaults to xover.min.cM.")
if( is.null(xover.min.cM) & is.null(xover.min.cM.male) & !is.null(xover.min.cM.female)) stop ("Crossover interference parameter only defined in one sex. Use xover.min.cM= ... ")
if( is.null(xover.min.cM) & !is.null(xover.min.cM.male) & is.null(xover.min.cM.female)) stop ("Crossover interference parameter only defined in one sex. Use xover.min.cM= ...")
if(!is.null(xover.min.cM) & is.null(xover.min.cM.male) & is.null(xover.min.cM.female)) message("Assuming that crossover interference is equal in both sexes.")
#~~ Use r if no sex specific information given
if(is.null(xover.min.cM.male )) xover.min.cM.male <- xover.min.cM
if(is.null(xover.min.cM.female)) xover.min.cM.female <- xover.min.cM
message("Assuming 1cM is equivalent to r = 0.01")
#~~ Tidy up the SNP names and founder MAFs etc. if not defined
if(is.null(snp.names)) snp.names <- paste0("SNP", 1:length(cM.male))
if(is.null(founder.mafs)){
message("Founder MAFs have not been defined - assuming MAF = 0.5")
founder.mafs <- rep(0.5, times = length(cM.male))
}
if(is.null(map.distances)){
message("Base pair distances have not been defined - Dummy distances used")
map.distances <- as.integer(cM.female*1e6)
}
if(is.null(chromosome.ids)){
message("Assuming all markers are on the same chromosome. Errors may occur when undefined")
chromosome.ids <- rep(1, length(cM.female))
}
#~~ RUN THE SIMULATIONS
#~~ Convert to R
r.male <- diff(cM.male/100)
r.female <- diff(cM.female/100)
r.male[which(r.male < 0)] <- 0.5
r.female[which(r.female < 0)] <- 0.5
#~~ Convert to r
xover.min.r.male <- xover.min.cM.male/100
xover.min.r.female <- xover.min.cM.female/100
#~~ Create a recombination template by sampling the probability of a crossover within an interval
r.cumu.female <- cumsum(r.female)
r.cumu.male <- cumsum(r.male)
suppressWarnings({
template.list <- sapply(transped$Parent.ID.SEX, function(x){
if(x == "MOTHER"){
remp.temp <- which(((runif(length(r.female)) < r.female) + 0L) == 1)
if(length(remp.temp) > 1 & !is.null(xover.min.r.female)){
while(min(diff(r.cumu.female[remp.temp])) < xover.min.r.female) {
remp.temp <- which(((runif(length(r.female)) < r.female) + 0L) == 1)
}
}
}
if(x == "FATHER"){
remp.temp <- which(((runif(length(r.male )) < r.male ) + 0L) == 1)
if(length(remp.temp) > 1 & !is.null(xover.min.r.male)){
while(min(diff(r.cumu.female[remp.temp])) < xover.min.r.male) {
remp.temp <- which(((runif(length(r.male)) < r.male) + 0L) == 1)
}
}
}
remp.temp
})
})
#~~ Add key to list
names(template.list) <- transped$Key
#~~ Calculate recombination count
transped$RecombCount <- unlist(lapply(template.list, length))
transped$RecombCount[which(transped$Cohort == 0)] <- NA
#~~ Create founder haplotypes by sampling allele frequencies
head(transped)
#~~ Create a list to sample haplotypes
haplo.list <- list()
haplo.list[1:length(unique(transped$Offspring.ID))] <- list(list(MOTHER = NA, FATHER = NA))
names(haplo.list) <- unique(as.character(transped$Offspring.ID))
#~~ Sample the founder haplotypes
system.time(for(i in which(transped$Cohort == 0)){
if(transped$Parent.ID.SEX[i] == "MOTHER") haplo.list[as.character(transped$Offspring.ID[i])][[1]]$MOTHER <- (runif(length(founder.mafs)) < founder.mafs) + 0L
if(transped$Parent.ID.SEX[i] == "FATHER") haplo.list[as.character(transped$Offspring.ID[i])][[1]]$FATHER <- (runif(length(founder.mafs)) < founder.mafs) + 0L
})
#~~ Sample the non-founder haplotypes by cohort. This loops through cohorts sequentially as
# parental haplotypes must exist before sampling.
for(cohort in 1:max(transped$Cohort)){
print(paste("Simulating haplotypes for cohort", cohort, "of", max(transped$Cohort)))
for(j in which(transped$Cohort == cohort)){
#~~ Pull out recombination information from template.list
rec.pos <- template.list[transped$Key[j]][[1]]
#~~ If there are no recombination events, sample one of the parental haplotypes at random
if(length(rec.pos) == 0){
haplo.list[as.character(transped$Offspring.ID[j])][[1]][transped$Parent.ID.SEX[j]] <- haplo.list[as.character(transped$Parent.ID[j])][[1]][sample.int(2, 1)]
}
#~~ If there are recombination events, sample the order of the parental haplotypes,
# exchange haplotypes and then transmit haplotype to offspring
if(length(rec.pos) > 0){
parental.haplotypes <- haplo.list[as.character(transped$Parent.ID[j])][[1]][sample.int(2, 2, replace = F)]
start.pos <- c(1, rec.pos[1:(length(rec.pos))] + 1)
stop.pos <- c(rec.pos, length(r.female) + 1)
fragments <- list()
for(k in 1:length(start.pos)){
if(k %% 2 != 0) fragments[[k]] <- parental.haplotypes[1][[1]][start.pos[k]:stop.pos[k]]
if(k %% 2 == 0) fragments[[k]] <- parental.haplotypes[2][[1]][start.pos[k]:stop.pos[k]]
}
haplo.list[as.character(transped$Offspring.ID[j])][[1]][transped$Parent.ID.SEX[j]] <- list(unlist(fragments))
}
}
}
#~~ Condense haplotypes into genotypes
genotype.list <- list()
if(phased.output == TRUE) message("Genotypes are phased - Maternal, Paternal")
for(i in 1:length(haplo.list)){
vec <- rep(NA, (length(r.female) + 1)*2)
if(phased.output == TRUE){
vec[seq(1, length(vec), 2)] <- haplo.list[[i]][1][[1]]
vec[seq(2, length(vec), 2)] <- haplo.list[[i]][2][[1]]
} else {
haplo.tab <- data.frame(A = haplo.list[[i]][1][[1]],
B = haplo.list[[i]][2][[1]])
vec <- as.vector(apply(haplo.tab, 1, sort))
}
genotype.list[[i]] <- vec
}
names(genotype.list) <- names(haplo.list)
x <- t(data.frame(genotype.list))
row.names(x) <- names(genotype.list)
colnames(x) <- rep(snp.names, each = 2)
x <- x + 1
#~~ Create errors in the dataset
geno.count <- nrow(x) * (ncol(x)/2)
error.count <- as.integer(geno.count * error.rate)
missing.count <- as.integer(geno.count * missing.rate)
if(error.count > 0){
errortemp <- data.frame(row = sample(1:nrow(x), replace = T, size = error.count),
col = sample(1:ncol(x), replace = T, size = error.count))
errortemp$col2 <- ifelse(errortemp$col %% 2 == 0, errortemp$col - 1, errortemp$col + 1)
for(i in 1:nrow(errortemp)){
tempgeno <- paste(x[errortemp$row[i],c(errortemp$col[i], errortemp$col2[i])], collapse = "")
if(tempgeno == "11") newgeno <- sample(c("12", "22"), size = 1)
if(tempgeno %in% c("12", "21")) newgeno <- sample(c("11", "22"), size = 1)
if(tempgeno == "22") newgeno <- sample(c("11", "12"), size = 1)
x[errortemp$row[i],c(errortemp$col[i], errortemp$col2[i])] <- as.vector(strsplit(newgeno, split = "")[[1]])
}
}
if(missing.count > 0){
missingtemp <- data.frame(row = sample(1:nrow(x), replace = T, size = missing.count),
col = sample(1:ncol(x), replace = T, size = missing.count))
missingtemp$col2 <- ifelse(missingtemp$col %% 2 == 0, missingtemp$col - 1, missingtemp$col + 1)
missingtemp <- data.frame(rbind(as.matrix(missingtemp[,c(1, 2)]),
as.matrix(missingtemp[,c(1, 3)])))
for(i in 1:nrow(missingtemp)) x[missingtemp$row[i],missingtemp$col[i]] <- 0
}
#~~ Create the return object in PLINK format
newx <- data.frame(FAMILY = 1, ANIMAL = row.names(x))
suppressMessages(newx <- join(newx, ped))
newx$SEX <- ifelse(newx$ANIMAL %in% newx$FATHER, 1, 0)
newx$PHENO <- -9
x <- cbind(newx, x)
mapobj <- data.frame(Chromosome = chromosome.ids,
SNP.Name = snp.names,
bpPosition = map.distances)
genabel.phenotab <- x[,c("ANIMAL", "SEX")]
names(genabel.phenotab) <- c("id", "sex")
#~~ generate a temporary file name
tempfile <- paste(sample(letters, replace = T, size = 10), collapse = "")
write.table(x, paste0(tempfile, ".ped"), row.names = F, col.names = F, quote = F)
write.table(mapobj, paste0(tempfile, ".map"), row.names = F, col.names = F, quote = F)
write.table(genabel.phenotab, paste0(tempfile, ".pheno"), row.names = F, quote = F)
convert.snp.ped(pedfile = paste0(tempfile, ".ped"),
mapfile = paste0(tempfile, ".map"),
outfile = paste0(tempfile, ".genabel"),
traits = 1, strand = "u", mapHasHeaderLine = F, bcast = F)
genabel.geno <- load.gwaa.data(genofile = paste0(tempfile, ".genabel"),
phenofile = paste0(tempfile, ".pheno"))
if(Sys.info()["sysname"] == "Windows") system("cmd", input = paste0("rm ", tempfile, "*"), show.output.on.console = F)
if(Sys.info()["sysname"] != "Windows") system(input = paste0("rm ", tempfile, "*"))
# return object
return(list(genabel.object = genabel.geno,
templates = template.list))
}
susjoh/simperSNP documentation built on May 30, 2019, 8:43 p.m.
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#' Simulate recombination events in R
#'
#' This function simulates genotypes within a pedigree, based on centiMorgan map
#' distances. Outputs genotypes as a GenABEL gwaa object.
#' @param ped data.frame of pedigree in "simple" format (Three columns for
#' ANIMAL, MOTHER and FATHER) or in "plink" format (Five to Six columns for
#' FAMILY, ANIMAL, FATHER, MOTHER, SEX and Phenotype, where the phenotype
#' column is optional). Is formatted within this function using
#' pedigree.format()
#' @param pedigree.type Character. "simple" or "plink"
#' @param cM Numeric vector of sex-averaged map distances in centiMorgans. Not
#' required if sex specific map distances are defined. First position should
#' be given as 0cM
#' @param cM.male Numeric vector of Male map distances in centiMorgans
#' @param cM.female Numeric vector of Female map distances in centiMorgans
#' @param founder.mafs Numeric vector of Minor allele frequencies for each
#' marker
#' @param chromosome.ids Vector of chromosome or linkage group IDs for each
#' marker
#' @param snp.names Optional character vector of SNP names
#' @param map.distances Optional vector of map distances in base pairs for
#' GenABEL object. Not used in the simulation.
#' @param error.rate numeric. Default is 1e-4. Future versions will allow vector.
#' @param missing.rate numeric. Default is 0.001. Future versions will allow vector.
#' @param xover.min.cM numeric. Minimum cM distance between two crossovers
#' @param xover.min.cM.male numeric. Minimum cM distance between two crossovers in males
#' @param xover.min.cM.female numeric. Minimum cM distance between two crossovers in females
simulateGenos <- function(ped,
pedigree.type = "simple",
cM = NULL,
cM.male = NULL,
cM.female = NULL,
founder.mafs = NULL,
chromosome.ids = NULL,
snp.names = NULL,
map.distances = NULL,
error.rate = 1e-4,
missing.rate = 0.001,
xover.min.cM = NULL,
xover.min.cM.male = NULL,
xover.min.cM.female = NULL){
require(plyr)
require(reshape2)
require(GenABEL)
require(kinship2)
phased.output <- TRUE
#~~ Format the pedigree
ped <- pedigree.format(ped, pedigree.type = pedigree.type)
#~~ melt pedfile to get a unique row for each gamete transfer
transped <- melt(ped[,c("ANIMAL", "MOTHER", "FATHER")], id.vars = "ANIMAL")
transped$variable <- as.character(transped$variable)
#~~ assign pedfile IDs to cohort and merge with transped
cohorts <- data.frame(ANIMAL = ped[,1],
Cohort = kindepth(ped[,1], ped[,3], ped[,2]))
suppressMessages(transped <- join(transped, cohorts))
#~~ Redefine columns
names(transped) <- c("Offspring.ID", "Parent.ID.SEX", "Parent.ID", "Cohort")
transped$Key <- paste(transped$Parent.ID, transped$Offspring.ID, sep = "_")
#~~ Recode founder gametes to 0 e.g. if one parent is unknown
transped$Cohort[which(transped$Parent.ID == 0)] <- 0
#~~ Check and format the recombination units
if( is.null(cM) & is.null(cM.male) & is.null(cM.female)) stop ("No recombination rates specified.")
if(!is.null(cM) & is.null(cM.male) & !is.null(cM.female)) warning("Recombination rate only defined in one sex. Missing sex defaults to r.")
if(!is.null(cM) & !is.null(cM.male) & is.null(cM.female)) warning("Recombination rate only defined in one sex. Missing sex defaults to r.")
if( is.null(cM) & is.null(cM.male) & !is.null(cM.female)) stop ("Recombination rate only defined in one sex. Use r= ... ")
if( is.null(cM) & !is.null(cM.male) & is.null(cM.female)) stop ("Recombination rate only defined in one sex. Use r= ...")
if(!is.null(cM) & is.null(cM.male) & is.null(cM.female)) message("Assuming that recombination rates are equal in both sexes.")
#~~ Use cM if no sex specific information given
if(is.null(cM.male )) cM.male <- cM
if(is.null(cM.female)) cM.female <- cM
#~~ Tidy up crossover interference parameters
if( is.null(xover.min.cM) & is.null(xover.min.cM.male) & is.null(xover.min.cM.female)) message ("Assuming no crossover interference.")
if(!is.null(xover.min.cM) & is.null(xover.min.cM.male) & !is.null(xover.min.cM.female)) warning("Crossover interference parameter only defined in one sex. Missing sex defaults to xover.min.cM.")
if(!is.null(xover.min.cM) & !is.null(xover.min.cM.male) & is.null(xover.min.cM.female)) warning("Crossover interference parameter only defined in one sex. Missing sex defaults to xover.min.cM.")
if( is.null(xover.min.cM) & is.null(xover.min.cM.male) & !is.null(xover.min.cM.female)) stop ("Crossover interference parameter only defined in one sex. Use xover.min.cM= ... ")
if( is.null(xover.min.cM) & !is.null(xover.min.cM.male) & is.null(xover.min.cM.female)) stop ("Crossover interference parameter only defined in one sex. Use xover.min.cM= ...")
if(!is.null(xover.min.cM) & is.null(xover.min.cM.male) & is.null(xover.min.cM.female)) message("Assuming that crossover interference is equal in both sexes.")
#~~ Use r if no sex specific information given
if(is.null(xover.min.cM.male )) xover.min.cM.male <- xover.min.cM
if(is.null(xover.min.cM.female)) xover.min.cM.female <- xover.min.cM
message("Assuming 1cM is equivalent to r = 0.01")
#~~ Tidy up the SNP names and founder MAFs etc. if not defined
if(is.null(snp.names)) snp.names <- paste0("SNP", 1:length(cM.male))
if(is.null(founder.mafs)){
message("Founder MAFs have not been defined - assuming MAF = 0.5")
founder.mafs <- rep(0.5, times = length(cM.male))
}
if(is.null(map.distances)){
message("Base pair distances have not been defined - Dummy distances used")
map.distances <- as.integer(cM.female*1e6)
}
if(is.null(chromosome.ids)){
message("Assuming all markers are on the same chromosome. Errors may occur when undefined")
chromosome.ids <- rep(1, length(cM.female))
}
#~~ RUN THE SIMULATIONS
#~~ Convert to R
r.male <- diff(cM.male/100)
r.female <- diff(cM.female/100)
r.male[which(r.male < 0)] <- 0.5
r.female[which(r.female < 0)] <- 0.5
#~~ Convert to r
xover.min.r.male <- xover.min.cM.male/100
xover.min.r.female <- xover.min.cM.female/100
#~~ Create a recombination template by sampling the probability of a crossover within an interval
r.cumu.female <- cumsum(r.female)
r.cumu.male <- cumsum(r.male)
suppressWarnings({
template.list <- sapply(transped$Parent.ID.SEX, function(x){
if(x == "MOTHER"){
remp.temp <- which(((runif(length(r.female)) < r.female) + 0L) == 1)
if(length(remp.temp) > 1 & !is.null(xover.min.r.female)){
while(min(diff(r.cumu.female[remp.temp])) < xover.min.r.female) {
remp.temp <- which(((runif(length(r.female)) < r.female) + 0L) == 1)
}
}
}
if(x == "FATHER"){
remp.temp <- which(((runif(length(r.male )) < r.male ) + 0L) == 1)
if(length(remp.temp) > 1 & !is.null(xover.min.r.male)){
while(min(diff(r.cumu.female[remp.temp])) < xover.min.r.male) {
remp.temp <- which(((runif(length(r.male)) < r.male) + 0L) == 1)
}
}
}
remp.temp
})
})
#~~ Add key to list
names(template.list) <- transped$Key
#~~ Calculate recombination count
transped$RecombCount <- unlist(lapply(template.list, length))
transped$RecombCount[which(transped$Cohort == 0)] <- NA
#~~ Create founder haplotypes by sampling allele frequencies
head(transped)
#~~ Create a list to sample haplotypes
haplo.list <- list()
haplo.list[1:length(unique(transped$Offspring.ID))] <- list(list(MOTHER = NA, FATHER = NA))
names(haplo.list) <- unique(as.character(transped$Offspring.ID))
#~~ Sample the founder haplotypes
system.time(for(i in which(transped$Cohort == 0)){
if(transped$Parent.ID.SEX[i] == "MOTHER") haplo.list[as.character(transped$Offspring.ID[i])][[1]]$MOTHER <- (runif(length(founder.mafs)) < founder.mafs) + 0L
if(transped$Parent.ID.SEX[i] == "FATHER") haplo.list[as.character(transped$Offspring.ID[i])][[1]]$FATHER <- (runif(length(founder.mafs)) < founder.mafs) + 0L
})
#~~ Sample the non-founder haplotypes by cohort. This loops through cohorts sequentially as
# parental haplotypes must exist before sampling.
for(cohort in 1:max(transped$Cohort)){
print(paste("Simulating haplotypes for cohort", cohort, "of", max(transped$Cohort)))
for(j in which(transped$Cohort == cohort)){
#~~ Pull out recombination information from template.list
rec.pos <- template.list[transped$Key[j]][[1]]
#~~ If there are no recombination events, sample one of the parental haplotypes at random
if(length(rec.pos) == 0){
haplo.list[as.character(transped$Offspring.ID[j])][[1]][transped$Parent.ID.SEX[j]] <- haplo.list[as.character(transped$Parent.ID[j])][[1]][sample.int(2, 1)]
}
#~~ If there are recombination events, sample the order of the parental haplotypes,
# exchange haplotypes and then transmit haplotype to offspring
if(length(rec.pos) > 0){
parental.haplotypes <- haplo.list[as.character(transped$Parent.ID[j])][[1]][sample.int(2, 2, replace = F)]
start.pos <- c(1, rec.pos[1:(length(rec.pos))] + 1)
stop.pos <- c(rec.pos, length(r.female) + 1)
fragments <- list()
for(k in 1:length(start.pos)){
if(k %% 2 != 0) fragments[[k]] <- parental.haplotypes[1][[1]][start.pos[k]:stop.pos[k]]
if(k %% 2 == 0) fragments[[k]] <- parental.haplotypes[2][[1]][start.pos[k]:stop.pos[k]]
}
haplo.list[as.character(transped$Offspring.ID[j])][[1]][transped$Parent.ID.SEX[j]] <- list(unlist(fragments))
}
}
}
#~~ Condense haplotypes into genotypes
genotype.list <- list()
if(phased.output == TRUE) message("Genotypes are phased - Maternal, Paternal")
for(i in 1:length(haplo.list)){
vec <- rep(NA, (length(r.female) + 1)*2)
if(phased.output == TRUE){
vec[seq(1, length(vec), 2)] <- haplo.list[[i]][1][[1]]
vec[seq(2, length(vec), 2)] <- haplo.list[[i]][2][[1]]
} else {
haplo.tab <- data.frame(A = haplo.list[[i]][1][[1]],
B = haplo.list[[i]][2][[1]])
vec <- as.vector(apply(haplo.tab, 1, sort))
}
genotype.list[[i]] <- vec
}
names(genotype.list) <- names(haplo.list)
x <- t(data.frame(genotype.list))
row.names(x) <- names(genotype.list)
colnames(x) <- rep(snp.names, each = 2)
x <- x + 1
#~~ Create errors in the dataset
geno.count <- nrow(x) * (ncol(x)/2)
error.count <- as.integer(geno.count * error.rate)
missing.count <- as.integer(geno.count * missing.rate)
if(error.count > 0){
errortemp <- data.frame(row = sample(1:nrow(x), replace = T, size = error.count),
col = sample(1:ncol(x), replace = T, size = error.count))
errortemp$col2 <- ifelse(errortemp$col %% 2 == 0, errortemp$col - 1, errortemp$col + 1)
for(i in 1:nrow(errortemp)){
tempgeno <- paste(x[errortemp$row[i],c(errortemp$col[i], errortemp$col2[i])], collapse = "")
if(tempgeno == "11") newgeno <- sample(c("12", "22"), size = 1)
if(tempgeno %in% c("12", "21")) newgeno <- sample(c("11", "22"), size = 1)
if(tempgeno == "22") newgeno <- sample(c("11", "12"), size = 1)
x[errortemp$row[i],c(errortemp$col[i], errortemp$col2[i])] <- as.vector(strsplit(newgeno, split = "")[[1]])
}
}
if(missing.count > 0){
missingtemp <- data.frame(row = sample(1:nrow(x), replace = T, size = missing.count),
col = sample(1:ncol(x), replace = T, size = missing.count))
missingtemp$col2 <- ifelse(missingtemp$col %% 2 == 0, missingtemp$col - 1, missingtemp$col + 1)
missingtemp <- data.frame(rbind(as.matrix(missingtemp[,c(1, 2)]),
as.matrix(missingtemp[,c(1, 3)])))
for(i in 1:nrow(missingtemp)) x[missingtemp$row[i],missingtemp$col[i]] <- 0
}
#~~ Create the return object in PLINK format
newx <- data.frame(FAMILY = 1, ANIMAL = row.names(x))
suppressMessages(newx <- join(newx, ped))
newx$SEX <- ifelse(newx$ANIMAL %in% newx$FATHER, 1, 0)
newx$PHENO <- -9
x <- cbind(newx, x)
mapobj <- data.frame(Chromosome = chromosome.ids,
SNP.Name = snp.names,
bpPosition = map.distances)
genabel.phenotab <- x[,c("ANIMAL", "SEX")]
names(genabel.phenotab) <- c("id", "sex")
#~~ generate a temporary file name
tempfile <- paste(sample(letters, replace = T, size = 10), collapse = "")
write.table(x, paste0(tempfile, ".ped"), row.names = F, col.names = F, quote = F)
write.table(mapobj, paste0(tempfile, ".map"), row.names = F, col.names = F, quote = F)
write.table(genabel.phenotab, paste0(tempfile, ".pheno"), row.names = F, quote = F)
convert.snp.ped(pedfile = paste0(tempfile, ".ped"),
mapfile = paste0(tempfile, ".map"),
outfile = paste0(tempfile, ".genabel"),
traits = 1, strand = "u", mapHasHeaderLine = F, bcast = F)
genabel.geno <- load.gwaa.data(genofile = paste0(tempfile, ".genabel"),
phenofile = paste0(tempfile, ".pheno"))
if(Sys.info()["sysname"] == "Windows") system("cmd", input = paste0("rm ", tempfile, "*"), show.output.on.console = F)
if(Sys.info()["sysname"] != "Windows") system(input = paste0("rm ", tempfile, "*"))
# return object
return(list(genabel.object = genabel.geno,
templates = template.list))
}
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