R/superceded/simPopulationLandscape.R
In susjoh/recomb.landscape: Simulate phenotypes under selection with variation in recombination phenotype.
#' Simulate a population with directional selection, recombination and landscape
#' variation
#'
#' @param map.dist A vector of cM distances between adjacent loci that are
#' sampled to create the recombination landscape. Can be any length greater
#' than the number of loci.
#' @param maf.info A vector of minor allele frequencies of trait loci. Can be
#' any length greater than the number of loci.
#' @param n.found.hap The number of haplotypes in the founder population.
#' Default is 100.
#' @param n.loci The number of loci contributing to phenotype. Assumes loci of
#' equal and additive effect on phenotype.
#' @param n.f The number of females in the founder population.
#' @param n.m The number of males in the founder population.
#' @param f.RS Female reproductive success. At present simulations only run with
#' a specific number of offspring for each female. This will be modified in
#' future.
#' @param f.RS.Pr Not currently used
#' @param sel.thresh.f Selection threshold in females (value between 0 and 1,
#' where 1 is all females selected)
#' @param sel.thresh.m Selection threshold in males (value between 0 and 1,
#' where 1 is all males selected)
#' @param prdm9.found.maf The minor allele frequence at the PRDM9 locus, which
#' will modify recombination landscape.
#' @param n.generations Number of generations to run the simulation
#' @param return.haplos Should the haplotype information be returned? Default =
#' FALSE as will return a large amount of data!
#' @param verbose (Default = TRUE) Should information on progress be printed?
#test values for optimisation:
# n.found.hap <- 100 # Number of founder haplotypes generated
# n.loci <- 100 # Number of loci underlying the trait
# n.f <- 100 # Number of females
# n.m <- 100 # Number of males
# f.RS <- 2 # Number of offspring per female
# f.RS.Pr <- 1 # Probability of number of offspring per female.
# sel.thresh.f <- 1 # Selection threshold
# sel.thresh.m <- 0.2 # Selection threshold
# prdm9.found.maf <- 0.4 # Starting freq of PRDM9
# n.generations <- 100
# n.iterations <- 100
# return.haplos <- TRUE
# restart.on.extinction <- TRUE
# progressBar = TRUE
#
# map <- read.table("data/soay_map.txt", header = T)
# map.dist <- diff(map$cM.Position.Sex.Averaged[seq(1, nrow(map), 10)])
# map.dist <- map.dist[which(map.dist >= 0 & map.dist < 2)]
# maf.info <- map$MAF
# SaveOnExtinction = TRUE
simPopulationLandscape<- function(
map.dist,
maf.info,
n.found.hap = 100,
n.loci,
n.f,
n.m,
f.RS,
f.RS.Pr = NULL,
sel.thresh.f,
sel.thresh.m,
prdm9.found.maf,
n.generations,
return.haplos = FALSE,
progressBar = TRUE,
SaveOnExtinction = FALSE,
FounderObject = NULL){
#~~ sample two landscapes and initial frequencies of alleles in founders
if(is.null(FounderObject)){
r1 <- sample(map.dist/100, replace = T, n.loci)
r2 <- sample(map.dist/100, replace = T, n.loci)
rhet <- (r1 + r2)/2
ggplot(data.frame(r = c(r1, rhet, r2), map = rep(1:3, each = length(r1)), x = rep(1:length(r1), times = 3)),
aes(x, r, colour = factor(map))) +
geom_line() +
scale_colour_brewer(palette = "Set1")
map.list <- list(r1, rhet, r2)
#~~ fix MAFs so that they show a range of frequencies between 0 & 1
mafs.found <- sample(maf.info, n.loci)
mafs.found <- mafs.found + (runif(n.loci) < 0.5)/2
#~~ determine PRDM9 genotype frequencies based on HWE
p <- prdm9.found.maf
q <- 1 - p
prdm9.found.prs <- c(p^2, 2*p*q, q^2)
#~~ Run Simulation
# NB Sex 1 = male, 2 = female (Xy, XX)
#~~ generate founder haplotypes
founder.haplos <- lapply(1:n.found.hap, function (x) (runif(n.loci) < mafs.found) + 0L)
#~~ generate diplotypes for n.f females and n.m males
gen.0 <- list()
gen.0[1:(n.f + n.m)] <- list(list(MOTHER = NA, FATHER = NA))
for(i in 1:(n.f + n.m)){
gen.0[[i]]["MOTHER"] <- sample(founder.haplos, size = 1)
gen.0[[i]]["FATHER"] <- sample(founder.haplos, size = 1)
}
#~~ create reference table
ref.0 <- data.frame(GEN = 0,
ID = 1:length(gen.0),
MOTHER = NA,
FATHER = NA,
#SEX = sapply(1:length(gen.0), function(x) (runif(1) < 0.5) + 1L),
SEX = rep(1:2, length.out = length(gen.0)),
PHENO = sapply(1:length(gen.0), function(x) sum(gen.0[[x]][[1]]) + sum(gen.0[[x]][[2]])),
PRDM9 = sapply(1:length(gen.0), function(x) sample(1:3, size = 1, prob = prdm9.found.prs)))
} else {
if(any(n.f != FounderObject$n.f,
n.m != FounderObject$n.m,
n.loci != FounderObject$n.loci)) stop("Founder Object parameters do not match those of current simulation")
ref.0 <- FounderObject$ref.0
founder.haplos <- founder.haplos <- FounderObject$founder.haplos
gen.0 <- FounderObject$gen.0
map.list<- FounderObject$map.list
#~~ determine PRDM9 genotype frequencies based on HWE
p <- prdm9.found.maf
q <- 1 - p
prdm9.found.prs <- c(p^2, 2*p*q, q^2)
ref.0$PRDM9 = sapply(1:length(gen.0), function(x) sample(1:3, size = 1, prob = prdm9.found.prs))
}
m.thresh <- sort(ref.0$PHENO[which(ref.0$SEX == 1)])[(1-sel.thresh.m)*length(ref.0$PHENO[which(ref.0$SEX == 1)])]
f.thresh <- sort(ref.0$PHENO[which(ref.0$SEX == 2)])[(1-sel.thresh.f)*length(ref.0$PHENO[which(ref.0$SEX == 2)])]
if(length(m.thresh) == 0) m.thresh <- 0
if(length(f.thresh) == 0) f.thresh <- 0
#~~ remove IDs that will not be selected
ref.0$Bred <- 0
ref.0$Bred[sort(c(which(ref.0$SEX == 1 & ref.0$PHENO >= m.thresh),
which(ref.0$SEX == 2 & ref.0$PHENO >= f.thresh)))] <- 1
results.list <- list()
haplo.list <- list()
results.list[[1]] <- ref.0
haplo.list[[1]] <- gen.0
#~~ generate spaces for diplotypes of two offspring per female and sample best fathers
if(progressBar == TRUE) pb = txtProgressBar(min = 1, max = n.generations, style = 3)
for(gen in 1:n.generations){
if(progressBar == TRUE) setTxtProgressBar(pb,gen)
length.out <- f.RS*length(which(ref.0$SEX == 2 & ref.0$Bred == 1))
if(any(c(length.out == 0, length(unique(ref.0$SEX)) == 1) == TRUE)) {
if(SaveOnExtinction == TRUE){
if(return.haplos == TRUE){
return(list(results = results.list, haplos = haplo.list, maps = map.list))
} else {
return(list(results = results.list, maps = map.list))
}
}
break(paste("Population has gone extinct at generation", gen))
}
ref.1 <- data.frame(GEN = gen,
ID = 1:length.out,
MOTHER = rep(ref.0$ID[which(ref.0$SEX == 2 & ref.0$Bred == 1)], each = f.RS),
FATHER = sample(ref.0$ID[which(ref.0$SEX == 1 & ref.0$Bred == 1)], size = length.out, replace = T),
SEX = (runif(length.out) < 0.5) + 1L,
PHENO = NA,
PRDM9 = NA)
#~~ Transmit a gamete from parents to offspring
gen.1 <- list()
gen.1[1:length.out] <- list(list(MOTHER = NA, FATHER = NA))
for(i in 1:length.out){
#~~ MOTHER ~~#
haplos <- gen.0 [[ref.1$MOTHER[i]]]
rmap <- map.list[[ref.0$PRDM9 [which(ref.0$ID == ref.1$MOTHER[i])]]]
#~~ sample crossover positions
rec.pos <- which(((runif(length(rmap)) < rmap) + 0L) == 1)
if(length(rmap) %in% rec.pos) rec.pos <- rec.pos[-which(rec.pos == length(rmap))]
if(length(rec.pos) == 0) gen.1[[i]]["MOTHER"] <- haplos[sample.int(2, 1)]
if(length(rec.pos) > 0){
haplos <- haplos[sample.int(2, 2, replace = F)]
start.pos <- c(1, rec.pos[1:(length(rec.pos))] + 1)
stop.pos <- c(rec.pos, length(rmap))
fragments <- list()
for(k in 1:length(start.pos)){
if(k %% 2 != 0) fragments[[k]] <- haplos[[1]][start.pos[k]:stop.pos[k]]
if(k %% 2 == 0) fragments[[k]] <- haplos[[2]][start.pos[k]:stop.pos[k]]
}
gen.1[[i]]["MOTHER"] <- list(unlist(fragments))
}
#~~ FATHER ~~#
haplos <- gen.0 [[ref.1$FATHER[i]]]
rmap <- map.list[[ref.0$PRDM9 [which(ref.0$ID == ref.1$FATHER[i])]]]
#~~ sample crossover positions
rec.pos <- which(((runif(length(rmap)) < rmap) + 0L) == 1)
if(length(rmap) %in% rec.pos) rec.pos <- rec.pos[-which(rec.pos == length(rmap))]
if(length(rec.pos) == 0) gen.1[[i]]["FATHER"] <- haplos[sample.int(2, 1)]
if(length(rec.pos) > 0){
haplos <- haplos[sample.int(2, 2, replace = F)]
start.pos <- c(1, rec.pos[1:(length(rec.pos))] + 1)
stop.pos <- c(rec.pos, length(rmap))
fragments <- list()
for(k in 1:length(start.pos)){
if(k %% 2 != 0) fragments[[k]] <- haplos[[1]][start.pos[k]:stop.pos[k]]
if(k %% 2 == 0) fragments[[k]] <- haplos[[2]][start.pos[k]:stop.pos[k]]
}
gen.1[[i]]["FATHER"] <- list(unlist(fragments))
}
#~~ Deal with PRDM9
prdm9.mum <- ref.0$PRDM9[which(ref.0$ID == ref.1$MOTHER[i])]
prdm9.mum.2 <- ifelse(prdm9.mum == 1, 0,
ifelse(prdm9.mum == 3, 1,
ifelse(prdm9.mum == 2, (runif(1) < 0.5) + 0L, NA)))
prdm9.dad <- ref.0$PRDM9[which(ref.0$ID == ref.1$FATHER[i])]
prdm9.dad.2 <- ifelse(prdm9.dad == 1, 0,
ifelse(prdm9.dad == 3, 1,
ifelse(prdm9.dad == 2, (runif(1) < 0.5) + 0L, NA)))
ref.1$PRDM9[i] <- prdm9.mum.2 + prdm9.dad.2 + 1
}
rm(haplos, rmap, rec.pos, start.pos, stop.pos, fragments, k, prdm9.mum, prdm9.mum.2, prdm9.dad, prdm9.dad.2)
ref.1$PHENO <- sapply(1:length(gen.1), function(x) sum(gen.1[[x]][[1]]) + sum(gen.1[[x]][[2]]))
#~~ Deal with IDs that will be selected
m.thresh <- sort(ref.1$PHENO[which(ref.1$SEX == 1)])[(1-sel.thresh.m)*length(ref.1$PHENO[which(ref.1$SEX == 1)])]
f.thresh <- sort(ref.1$PHENO[which(ref.1$SEX == 2)])[(1-sel.thresh.f)*length(ref.1$PHENO[which(ref.1$SEX == 2)])]
if(length(m.thresh) == 0) m.thresh <- 0
if(length(f.thresh) == 0) f.thresh <- 0
#~~ remove IDs that will not be selected
ref.1$Bred <- 0
ref.1$Bred[sort(c(which(ref.1$SEX == 1 & ref.1$PHENO >= m.thresh),
which(ref.1$SEX == 2 & ref.1$PHENO >= f.thresh)))] <- 1
results.list[[(gen + 1)]] <- ref.1
if(return.haplos == TRUE) haplo.list[[(gen + 1)]] <- gen.1
gen.0 <- gen.1
ref.0 <- ref.1
}
#~~ Parse output
if(return.haplos == TRUE){
list(results = results.list, haplos = haplo.list, maps = map.list)
} else {
list(results = results.list, maps = map.list)
}
}
susjoh/recomb.landscape documentation built on May 30, 2019, 8:43 p.m.
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#' Simulate a population with directional selection, recombination and landscape
#' variation
#'
#' @param map.dist A vector of cM distances between adjacent loci that are
#' sampled to create the recombination landscape. Can be any length greater
#' than the number of loci.
#' @param maf.info A vector of minor allele frequencies of trait loci. Can be
#' any length greater than the number of loci.
#' @param n.found.hap The number of haplotypes in the founder population.
#' Default is 100.
#' @param n.loci The number of loci contributing to phenotype. Assumes loci of
#' equal and additive effect on phenotype.
#' @param n.f The number of females in the founder population.
#' @param n.m The number of males in the founder population.
#' @param f.RS Female reproductive success. At present simulations only run with
#' a specific number of offspring for each female. This will be modified in
#' future.
#' @param f.RS.Pr Not currently used
#' @param sel.thresh.f Selection threshold in females (value between 0 and 1,
#' where 1 is all females selected)
#' @param sel.thresh.m Selection threshold in males (value between 0 and 1,
#' where 1 is all males selected)
#' @param prdm9.found.maf The minor allele frequence at the PRDM9 locus, which
#' will modify recombination landscape.
#' @param n.generations Number of generations to run the simulation
#' @param return.haplos Should the haplotype information be returned? Default =
#' FALSE as will return a large amount of data!
#' @param verbose (Default = TRUE) Should information on progress be printed?
#test values for optimisation:
# n.found.hap <- 100 # Number of founder haplotypes generated
# n.loci <- 100 # Number of loci underlying the trait
# n.f <- 100 # Number of females
# n.m <- 100 # Number of males
# f.RS <- 2 # Number of offspring per female
# f.RS.Pr <- 1 # Probability of number of offspring per female.
# sel.thresh.f <- 1 # Selection threshold
# sel.thresh.m <- 0.2 # Selection threshold
# prdm9.found.maf <- 0.4 # Starting freq of PRDM9
# n.generations <- 100
# n.iterations <- 100
# return.haplos <- TRUE
# restart.on.extinction <- TRUE
# progressBar = TRUE
#
# map <- read.table("data/soay_map.txt", header = T)
# map.dist <- diff(map$cM.Position.Sex.Averaged[seq(1, nrow(map), 10)])
# map.dist <- map.dist[which(map.dist >= 0 & map.dist < 2)]
# maf.info <- map$MAF
# SaveOnExtinction = TRUE
simPopulationLandscape<- function(
map.dist,
maf.info,
n.found.hap = 100,
n.loci,
n.f,
n.m,
f.RS,
f.RS.Pr = NULL,
sel.thresh.f,
sel.thresh.m,
prdm9.found.maf,
n.generations,
return.haplos = FALSE,
progressBar = TRUE,
SaveOnExtinction = FALSE,
FounderObject = NULL){
#~~ sample two landscapes and initial frequencies of alleles in founders
if(is.null(FounderObject)){
r1 <- sample(map.dist/100, replace = T, n.loci)
r2 <- sample(map.dist/100, replace = T, n.loci)
rhet <- (r1 + r2)/2
ggplot(data.frame(r = c(r1, rhet, r2), map = rep(1:3, each = length(r1)), x = rep(1:length(r1), times = 3)),
aes(x, r, colour = factor(map))) +
geom_line() +
scale_colour_brewer(palette = "Set1")
map.list <- list(r1, rhet, r2)
#~~ fix MAFs so that they show a range of frequencies between 0 & 1
mafs.found <- sample(maf.info, n.loci)
mafs.found <- mafs.found + (runif(n.loci) < 0.5)/2
#~~ determine PRDM9 genotype frequencies based on HWE
p <- prdm9.found.maf
q <- 1 - p
prdm9.found.prs <- c(p^2, 2*p*q, q^2)
#~~ Run Simulation
# NB Sex 1 = male, 2 = female (Xy, XX)
#~~ generate founder haplotypes
founder.haplos <- lapply(1:n.found.hap, function (x) (runif(n.loci) < mafs.found) + 0L)
#~~ generate diplotypes for n.f females and n.m males
gen.0 <- list()
gen.0[1:(n.f + n.m)] <- list(list(MOTHER = NA, FATHER = NA))
for(i in 1:(n.f + n.m)){
gen.0[[i]]["MOTHER"] <- sample(founder.haplos, size = 1)
gen.0[[i]]["FATHER"] <- sample(founder.haplos, size = 1)
}
#~~ create reference table
ref.0 <- data.frame(GEN = 0,
ID = 1:length(gen.0),
MOTHER = NA,
FATHER = NA,
#SEX = sapply(1:length(gen.0), function(x) (runif(1) < 0.5) + 1L),
SEX = rep(1:2, length.out = length(gen.0)),
PHENO = sapply(1:length(gen.0), function(x) sum(gen.0[[x]][[1]]) + sum(gen.0[[x]][[2]])),
PRDM9 = sapply(1:length(gen.0), function(x) sample(1:3, size = 1, prob = prdm9.found.prs)))
} else {
if(any(n.f != FounderObject$n.f,
n.m != FounderObject$n.m,
n.loci != FounderObject$n.loci)) stop("Founder Object parameters do not match those of current simulation")
ref.0 <- FounderObject$ref.0
founder.haplos <- founder.haplos <- FounderObject$founder.haplos
gen.0 <- FounderObject$gen.0
map.list<- FounderObject$map.list
#~~ determine PRDM9 genotype frequencies based on HWE
p <- prdm9.found.maf
q <- 1 - p
prdm9.found.prs <- c(p^2, 2*p*q, q^2)
ref.0$PRDM9 = sapply(1:length(gen.0), function(x) sample(1:3, size = 1, prob = prdm9.found.prs))
}
m.thresh <- sort(ref.0$PHENO[which(ref.0$SEX == 1)])[(1-sel.thresh.m)*length(ref.0$PHENO[which(ref.0$SEX == 1)])]
f.thresh <- sort(ref.0$PHENO[which(ref.0$SEX == 2)])[(1-sel.thresh.f)*length(ref.0$PHENO[which(ref.0$SEX == 2)])]
if(length(m.thresh) == 0) m.thresh <- 0
if(length(f.thresh) == 0) f.thresh <- 0
#~~ remove IDs that will not be selected
ref.0$Bred <- 0
ref.0$Bred[sort(c(which(ref.0$SEX == 1 & ref.0$PHENO >= m.thresh),
which(ref.0$SEX == 2 & ref.0$PHENO >= f.thresh)))] <- 1
results.list <- list()
haplo.list <- list()
results.list[[1]] <- ref.0
haplo.list[[1]] <- gen.0
#~~ generate spaces for diplotypes of two offspring per female and sample best fathers
if(progressBar == TRUE) pb = txtProgressBar(min = 1, max = n.generations, style = 3)
for(gen in 1:n.generations){
if(progressBar == TRUE) setTxtProgressBar(pb,gen)
length.out <- f.RS*length(which(ref.0$SEX == 2 & ref.0$Bred == 1))
if(any(c(length.out == 0, length(unique(ref.0$SEX)) == 1) == TRUE)) {
if(SaveOnExtinction == TRUE){
if(return.haplos == TRUE){
return(list(results = results.list, haplos = haplo.list, maps = map.list))
} else {
return(list(results = results.list, maps = map.list))
}
}
break(paste("Population has gone extinct at generation", gen))
}
ref.1 <- data.frame(GEN = gen,
ID = 1:length.out,
MOTHER = rep(ref.0$ID[which(ref.0$SEX == 2 & ref.0$Bred == 1)], each = f.RS),
FATHER = sample(ref.0$ID[which(ref.0$SEX == 1 & ref.0$Bred == 1)], size = length.out, replace = T),
SEX = (runif(length.out) < 0.5) + 1L,
PHENO = NA,
PRDM9 = NA)
#~~ Transmit a gamete from parents to offspring
gen.1 <- list()
gen.1[1:length.out] <- list(list(MOTHER = NA, FATHER = NA))
for(i in 1:length.out){
#~~ MOTHER ~~#
haplos <- gen.0 [[ref.1$MOTHER[i]]]
rmap <- map.list[[ref.0$PRDM9 [which(ref.0$ID == ref.1$MOTHER[i])]]]
#~~ sample crossover positions
rec.pos <- which(((runif(length(rmap)) < rmap) + 0L) == 1)
if(length(rmap) %in% rec.pos) rec.pos <- rec.pos[-which(rec.pos == length(rmap))]
if(length(rec.pos) == 0) gen.1[[i]]["MOTHER"] <- haplos[sample.int(2, 1)]
if(length(rec.pos) > 0){
haplos <- haplos[sample.int(2, 2, replace = F)]
start.pos <- c(1, rec.pos[1:(length(rec.pos))] + 1)
stop.pos <- c(rec.pos, length(rmap))
fragments <- list()
for(k in 1:length(start.pos)){
if(k %% 2 != 0) fragments[[k]] <- haplos[[1]][start.pos[k]:stop.pos[k]]
if(k %% 2 == 0) fragments[[k]] <- haplos[[2]][start.pos[k]:stop.pos[k]]
}
gen.1[[i]]["MOTHER"] <- list(unlist(fragments))
}
#~~ FATHER ~~#
haplos <- gen.0 [[ref.1$FATHER[i]]]
rmap <- map.list[[ref.0$PRDM9 [which(ref.0$ID == ref.1$FATHER[i])]]]
#~~ sample crossover positions
rec.pos <- which(((runif(length(rmap)) < rmap) + 0L) == 1)
if(length(rmap) %in% rec.pos) rec.pos <- rec.pos[-which(rec.pos == length(rmap))]
if(length(rec.pos) == 0) gen.1[[i]]["FATHER"] <- haplos[sample.int(2, 1)]
if(length(rec.pos) > 0){
haplos <- haplos[sample.int(2, 2, replace = F)]
start.pos <- c(1, rec.pos[1:(length(rec.pos))] + 1)
stop.pos <- c(rec.pos, length(rmap))
fragments <- list()
for(k in 1:length(start.pos)){
if(k %% 2 != 0) fragments[[k]] <- haplos[[1]][start.pos[k]:stop.pos[k]]
if(k %% 2 == 0) fragments[[k]] <- haplos[[2]][start.pos[k]:stop.pos[k]]
}
gen.1[[i]]["FATHER"] <- list(unlist(fragments))
}
#~~ Deal with PRDM9
prdm9.mum <- ref.0$PRDM9[which(ref.0$ID == ref.1$MOTHER[i])]
prdm9.mum.2 <- ifelse(prdm9.mum == 1, 0,
ifelse(prdm9.mum == 3, 1,
ifelse(prdm9.mum == 2, (runif(1) < 0.5) + 0L, NA)))
prdm9.dad <- ref.0$PRDM9[which(ref.0$ID == ref.1$FATHER[i])]
prdm9.dad.2 <- ifelse(prdm9.dad == 1, 0,
ifelse(prdm9.dad == 3, 1,
ifelse(prdm9.dad == 2, (runif(1) < 0.5) + 0L, NA)))
ref.1$PRDM9[i] <- prdm9.mum.2 + prdm9.dad.2 + 1
}
rm(haplos, rmap, rec.pos, start.pos, stop.pos, fragments, k, prdm9.mum, prdm9.mum.2, prdm9.dad, prdm9.dad.2)
ref.1$PHENO <- sapply(1:length(gen.1), function(x) sum(gen.1[[x]][[1]]) + sum(gen.1[[x]][[2]]))
#~~ Deal with IDs that will be selected
m.thresh <- sort(ref.1$PHENO[which(ref.1$SEX == 1)])[(1-sel.thresh.m)*length(ref.1$PHENO[which(ref.1$SEX == 1)])]
f.thresh <- sort(ref.1$PHENO[which(ref.1$SEX == 2)])[(1-sel.thresh.f)*length(ref.1$PHENO[which(ref.1$SEX == 2)])]
if(length(m.thresh) == 0) m.thresh <- 0
if(length(f.thresh) == 0) f.thresh <- 0
#~~ remove IDs that will not be selected
ref.1$Bred <- 0
ref.1$Bred[sort(c(which(ref.1$SEX == 1 & ref.1$PHENO >= m.thresh),
which(ref.1$SEX == 2 & ref.1$PHENO >= f.thresh)))] <- 1
results.list[[(gen + 1)]] <- ref.1
if(return.haplos == TRUE) haplo.list[[(gen + 1)]] <- gen.1
gen.0 <- gen.1
ref.0 <- ref.1
}
#~~ Parse output
if(return.haplos == TRUE){
list(results = results.list, haplos = haplo.list, maps = map.list)
} else {
list(results = results.list, maps = map.list)
}
}
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