haplovalidate: Validate reconstructed haplotypes
In kathrinannaotte/haplovalidate: Detecting selected haplotype blocks in Evolve and Resequence experiments
View source: R/haplovalidate.R
haplovalidate R Documentation
Validate reconstructed haplotypes
Description
under consrtruction
Usage
haplovalidate(cands, cmh, parameters, repl, gens, takerandom, filterrange)
Arguments
cands
cmh
parameters
repl
gens
takerandom
filterrange
Examples
##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (cands, cmh, parameters, repl, gens, takerandom, filterrange)
{
base.pops <- c(rep(TRUE, length(repl)), rep(FALSE, length(repl) *
(length(gens) - 1)))
compare <- c(rep(rep(TRUE, length(gens)), length(repl)))
min.minor.freq <- 0
max.minor.freq <- 1
minfreqchange <- 0
minrepl <- 1
min.lib.frac <- 0.75
thres.ttest <- 0.025
min.cl.size <- 20
min.inter <- ceiling(min.cl.size/5)
transform.af <- function(af) {
af.sqrt <- asin(sqrt(af))
af.transf <- t(af.sqrt)
af.scale <- scale(af.transf, center = TRUE, scale = TRUE)
return(af.scale)
}
chromis <- unique(cands$chr)
final <- c()
hapval.result <- list()
if (length(chromis) > 1) {
cluster.snps <- c()
for (chromo in chromis) {
winsize <- round(as.numeric(parameters[chr == chromo,
win]) * 1e+06, 0)
ts <- initialize_SNP_time_series(chr = cands[chr ==
chromo, chr], pos = cands[chr == chromo, pos],
base.freq = cands[chr == chromo, basePops], lib.freqs = cands[chr ==
chromo, 7:(ncol(cands)), with = F], pop.ident = c(rep(repl,
each = length(gens))), pop.generation = rep(gens,
length(repl)), use.libs = compare, min.minor.freq = min.minor.freq,
max.minor.freq = max.minor.freq, winsize = winsize,
win.scale = "bp", min.lib.frac = min.lib.frac,
minfreqchange = minfreqchange, minrepl = minrepl)
for (c in rev(seq(0.3, 0.9, 0.1))) {
print(paste("chr ", chromo, " cluster corr ",
c, sep = ""))
hbs <- reconstruct_hb(ts, chrom = chromo, min.cl.size = min.cl.size,
min.cl.cor = c, min.inter = min.inter, single.win = T,
transf = TRUE, arcsine = TRUE, scaleSNP = TRUE)
print(paste("found ", number_hbr(hbs), " clusters",
sep = ""))
if (number_hbr(hbs) > 0) {
summi <- nrow(summary(hbs))
for (k in 1:summi) {
snpis <- data.table(cbind(chromo, c, k, markers(hbs,
k)))
colnames(snpis) <- c("chr", "corr", "clust",
"pos")
ids <- snpis[, .(chr, corr, clust)]
taggis <- apply(ids, 1, paste, collapse = "_")
snpis[, `:=`(tag, taggis)]
cluster.snps <- rbind(cluster.snps, snpis)
}
}
}
}
cluster.snps <- data.table(cluster.snps)
check.cor.raw <- unique(cluster.snps[, .(chr, clust,
corr)])
check.cor.raw <- data.table(check.cor.raw)
check.cor <- check.cor.raw[, .N, by = .(chr, corr)]
for (a in chromis) {
check.cor.sub <- check.cor[chr != a & N != 0, corr]
min.cl.cor <- as.numeric(check.cor[chr == a & corr %in%
check.cor.sub, corr[which.max(N)]])
rawsteps <- 0.05
finesteps <- 0.01
clusteron <- TRUE
noT <- TRUE
search <- FALSE
raw <- TRUE
succesful <- FALSE
old.cl.cor <- 0
while (clusteron) {
alreadythere <- cluster.snps[corr == min.cl.cor]
if (nrow(alreadythere) == 0) {
for (b in chromis) {
winsize <- round(as.numeric(parameters[chr ==
b, win]) * 1e+06, 0)
min.inter <- ceiling(min.cl.size/5)
ts <- initialize_SNP_time_series(chr = cands[chr ==
b, chr], pos = cands[chr == b, pos], base.freq = cands[chr ==
b, basePops], lib.freqs = cands[chr ==
b, 7:(ncol(cands)), with = F], pop.ident = c(rep(repl,
each = length(gens))), pop.generation = rep(gens,
length(repl)), use.libs = compare, min.minor.freq = min.minor.freq,
max.minor.freq = max.minor.freq, winsize = winsize,
win.scale = "bp", min.lib.frac = min.lib.frac,
minfreqchange = minfreqchange, minrepl = minrepl)
hbs <- reconstruct_hb(ts, chrom = b, min.cl.size = min.cl.size,
min.cl.cor = min.cl.cor, min.inter = min.inter,
single.win = T, transf = TRUE, arcsine = TRUE,
scaleSNP = TRUE)
print(paste("found ", number_hbr(hbs), " clusters",
sep = ""))
if (number_hbr(hbs) > 0) {
summi <- nrow(summary(hbs))
for (k in 1:summi) {
snpis <- data.table(cbind(b, min.cl.cor,
k, markers(hbs, k)))
colnames(snpis) <- c("chr", "corr", "clust",
"pos")
ids <- snpis[, .(chr, corr, clust)]
taggis <- apply(ids, 1, paste, collapse = "_")
snpis[, `:=`(tag, taggis)]
cluster.snps <- rbind(cluster.snps, snpis)
}
}
}
}
ids <- cluster.snps[, .(chr, corr, clust)]
taggis <- apply(ids, 1, paste, collapse = "_")
cluster.snps[, `:=`(tag, taggis)]
cluster.snps.sub <- cluster.snps[corr == min.cl.cor]
check.cluster.snps.sub <- cluster.snps.sub[,
.N, by = chr]
if (nrow(check.cluster.snps.sub) > 1) {
cluster.snps.sub[, `:=`(pos, as.numeric(pos))]
cluster.ord <- cluster.snps.sub[order(as.numeric(pos)),
.SD, by = .(chr, corr)]
ids <- cluster.ord[, .(chr, corr, clust)]
taggis <- apply(ids, 1, paste, collapse = "_")
cand.clust <- merge(cluster.ord, cands, by = c("chr",
"pos"))
afcolis <- grep("L[0-9]", colnames(cand.clust),
value = TRUE)
clust.tags.foc <- unique(cand.clust[chr ==
a, tag])
clust.tags <- unique(cand.clust[, tag])
combi.tab <- c()
corfoc <- c()
corcomp <- c()
print(paste("corr ", min.cl.cor))
for (i in clust.tags.foc) {
cat(".")
cluster.sub.i.raw <- cand.clust[tag == i,
]
if (nrow(cluster.sub.i.raw) > takerandom) {
red.pos <- sample(cluster.sub.i.raw$pos,
takerandom)
pos.indi <- cluster.sub.i.raw$pos %in%
red.pos
cluster.sub.i <- cluster.sub.i.raw[pos.indi,
]
}
else cluster.sub.i <- cluster.sub.i.raw
cluster.af.i <- cluster.sub.i[, afcolis,
with = FALSE]
cluster.scale.i <- transform.af(cluster.af.i)
minmax <- cand.clust[tag == i, .(min(pos),
max(pos))]
left <- na.exclude(unique(c(rev(cand.clust[chr ==
a & pos >= minmax[, V1] - winsize & pos <
minmax[, V2], tag]))))
right <- na.exclude(unique(cand.clust[chr ==
a & pos > minmax[, V2] & pos <= minmax[,
V2] + winsize, tag]))
neighbours <- unique(c(left[left != i], right))
if (length(neighbours) > 0) {
combi.tab.sub <- cbind(i, neighbours)
if (length(combi.tab) > 0) {
check1 <- combi.tab.sub[, 1] %in% combi.tab[,
2]
check2 <- combi.tab.sub[, 2] %in% combi.tab[,
1]
red.indi <- apply(cbind(check1, check2),
1, all)
combis <- combi.tab.sub[red.indi == F,
"neighbours"]
combi.tab <- rbind(combi.tab, combi.tab.sub[red.indi ==
F, ])
}
else {
combis <- combi.tab.sub[, "neighbours"]
combi.tab <- rbind(combi.tab, combi.tab.sub)
}
}
else {
combis <- c()
}
compare.raw <- clust.tags[grep(paste("^",
a, sep = ""), clust.tags, invert = T)]
compare.clust <- c(combis, compare.raw)
comclust <- function(x) {
sub <- cand.clust[tag == x, ]
if (nrow(sub) > takerandom) {
red.pos <- sample(sub$pos, takerandom)
pos.indi <- sub$pos %in% red.pos
sub <- sub[pos.indi, ]
}
cluster.af <- sub[, afcolis, with = FALSE]
cluster.scale <- transform.af(cluster.af)
clustcor.sub <- median(cor(cluster.scale.i,
cluster.scale), na.rm = TRUE)
return(clustcor.sub)
}
clustcor <- sapply(compare.clust, comclust)
corfoc <- c(corfoc, clustcor[compare.clust %in%
combis])
corcomp <- c(corcomp, clustcor[compare.clust %in%
combis == F])
}
}
if (length(corfoc) > 3 & length(corcomp) > 3) {
t <- t.test(fisherz(corfoc), fisherz(corcomp),
"greater")
p <- t$p.value
print(paste(" pval ", round(p, 4), " for chr ",
a, sep = ""))
if (p <= thres.ttest) {
old.cl.cor <- min.cl.cor
if (raw & search == F) {
min.cl.cor <- min.cl.cor - rawsteps
noT <- F
}
else {
min.cl.cor <- min.cl.cor - finesteps
raw <- FALSE
noT <- F
}
}
else {
if (raw & noT == F) {
raw <- F
min.cl.cor <- min.cl.cor + rawsteps - finesteps
}
else {
if (raw == F & noT == F) {
print("Normal")
clusteron <- FALSE
successful <- TRUE
cluster.final.sub <- cluster.snps[corr ==
old.cl.cor & chr == a]
final <- rbind(final, cluster.final.sub)
print(paste("chr ", a, " done", sep = ""))
}
}
if (raw & noT) {
min.cl.cor <- min.cl.cor + finesteps
search <- T
}
}
}
else {
if (search) {
print("search")
clusteron <- FALSE
successful <- TRUE
final.raw <- cluster.snps[corr == old.cl.cor &
chr == a, `:=`(tag, paste(tag, "_search",
sep = ""))]
final <- rbind(final, final.raw)
print(paste("chr ", a, " done", sep = ""))
}
else {
if (noT)
min.cl.cor <- min.cl.cor + finesteps
else min.cl.cor <- min.cl.cor - finesteps
}
}
if (min.cl.cor < 0.1 | min.cl.cor > 1) {
clusteron <- FALSE
successful <- TRUE
final.raw <- cluster.snps[corr == old.cl.cor &
chr == a, `:=`(tag, paste(tag, "_no_conv",
sep = ""))]
final <- rbind(final, final.raw[chr == a])
print(paste("chr ", a, " done; no convergence!",
sep = ""))
}
}
}
}
final[, `:=`(pos, as.numeric(pos))]
hapval.result[["all_haplotypes"]] <- final
red <- list()
if (final != "clustering impossible") {
corris <- unique(final[, .(chr, corr)])[, corr]
finish <- unique(str_extract(final[, tag], "no_.*"))
if (length(finish) == 1)
finish <- rep(finish, 2)
cands.cmh <- merge(cands, cmh, by = c("chr", "pos"))
datcmh <- merge(final, cands.cmh, by = c("chr", "pos"),
all = T)
datcmh.ord <- datcmh[order(score, decreasing = T)]
for (i in na.exclude(unique(datcmh.ord$tag))) {
x.sub <- final[tag == i]
minmax <- x.sub[, .(min(pos), max(pos))]
chr.sub <- unique(x.sub$chr)
maxtag <- datcmh.ord[pos >= minmax[, V1] - filterrange &
pos <= minmax[, V2] + filterrange & chr == chr.sub &
is.na(tag) == F, tag[which.max(score)]]
if (i != maxtag)
datcmh.ord[tag == i, `:=`(tag, NA)]
}
mergomat <- datcmh.ord[, .(chr, pos, score, tag)]
mergomat.final <- mergomat[is.na(tag) == F]
hapval.result[["dominant_haplotypes"]] <- mergomat.final
}
return(hapval.result)
}
kathrinannaotte/haplovalidate documentation built on May 21, 2022, midnight
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View source: R/haplovalidate.R
| haplovalidate | R Documentation |
Validate reconstructed haplotypes
Description
under consrtruction
Usage
haplovalidate(cands, cmh, parameters, repl, gens, takerandom, filterrange)
Arguments
cands |
|
cmh |
|
parameters |
|
repl |
|
gens |
|
takerandom |
|
filterrange |
Examples
##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (cands, cmh, parameters, repl, gens, takerandom, filterrange)
{
base.pops <- c(rep(TRUE, length(repl)), rep(FALSE, length(repl) *
(length(gens) - 1)))
compare <- c(rep(rep(TRUE, length(gens)), length(repl)))
min.minor.freq <- 0
max.minor.freq <- 1
minfreqchange <- 0
minrepl <- 1
min.lib.frac <- 0.75
thres.ttest <- 0.025
min.cl.size <- 20
min.inter <- ceiling(min.cl.size/5)
transform.af <- function(af) {
af.sqrt <- asin(sqrt(af))
af.transf <- t(af.sqrt)
af.scale <- scale(af.transf, center = TRUE, scale = TRUE)
return(af.scale)
}
chromis <- unique(cands$chr)
final <- c()
hapval.result <- list()
if (length(chromis) > 1) {
cluster.snps <- c()
for (chromo in chromis) {
winsize <- round(as.numeric(parameters[chr == chromo,
win]) * 1e+06, 0)
ts <- initialize_SNP_time_series(chr = cands[chr ==
chromo, chr], pos = cands[chr == chromo, pos],
base.freq = cands[chr == chromo, basePops], lib.freqs = cands[chr ==
chromo, 7:(ncol(cands)), with = F], pop.ident = c(rep(repl,
each = length(gens))), pop.generation = rep(gens,
length(repl)), use.libs = compare, min.minor.freq = min.minor.freq,
max.minor.freq = max.minor.freq, winsize = winsize,
win.scale = "bp", min.lib.frac = min.lib.frac,
minfreqchange = minfreqchange, minrepl = minrepl)
for (c in rev(seq(0.3, 0.9, 0.1))) {
print(paste("chr ", chromo, " cluster corr ",
c, sep = ""))
hbs <- reconstruct_hb(ts, chrom = chromo, min.cl.size = min.cl.size,
min.cl.cor = c, min.inter = min.inter, single.win = T,
transf = TRUE, arcsine = TRUE, scaleSNP = TRUE)
print(paste("found ", number_hbr(hbs), " clusters",
sep = ""))
if (number_hbr(hbs) > 0) {
summi <- nrow(summary(hbs))
for (k in 1:summi) {
snpis <- data.table(cbind(chromo, c, k, markers(hbs,
k)))
colnames(snpis) <- c("chr", "corr", "clust",
"pos")
ids <- snpis[, .(chr, corr, clust)]
taggis <- apply(ids, 1, paste, collapse = "_")
snpis[, `:=`(tag, taggis)]
cluster.snps <- rbind(cluster.snps, snpis)
}
}
}
}
cluster.snps <- data.table(cluster.snps)
check.cor.raw <- unique(cluster.snps[, .(chr, clust,
corr)])
check.cor.raw <- data.table(check.cor.raw)
check.cor <- check.cor.raw[, .N, by = .(chr, corr)]
for (a in chromis) {
check.cor.sub <- check.cor[chr != a & N != 0, corr]
min.cl.cor <- as.numeric(check.cor[chr == a & corr %in%
check.cor.sub, corr[which.max(N)]])
rawsteps <- 0.05
finesteps <- 0.01
clusteron <- TRUE
noT <- TRUE
search <- FALSE
raw <- TRUE
succesful <- FALSE
old.cl.cor <- 0
while (clusteron) {
alreadythere <- cluster.snps[corr == min.cl.cor]
if (nrow(alreadythere) == 0) {
for (b in chromis) {
winsize <- round(as.numeric(parameters[chr ==
b, win]) * 1e+06, 0)
min.inter <- ceiling(min.cl.size/5)
ts <- initialize_SNP_time_series(chr = cands[chr ==
b, chr], pos = cands[chr == b, pos], base.freq = cands[chr ==
b, basePops], lib.freqs = cands[chr ==
b, 7:(ncol(cands)), with = F], pop.ident = c(rep(repl,
each = length(gens))), pop.generation = rep(gens,
length(repl)), use.libs = compare, min.minor.freq = min.minor.freq,
max.minor.freq = max.minor.freq, winsize = winsize,
win.scale = "bp", min.lib.frac = min.lib.frac,
minfreqchange = minfreqchange, minrepl = minrepl)
hbs <- reconstruct_hb(ts, chrom = b, min.cl.size = min.cl.size,
min.cl.cor = min.cl.cor, min.inter = min.inter,
single.win = T, transf = TRUE, arcsine = TRUE,
scaleSNP = TRUE)
print(paste("found ", number_hbr(hbs), " clusters",
sep = ""))
if (number_hbr(hbs) > 0) {
summi <- nrow(summary(hbs))
for (k in 1:summi) {
snpis <- data.table(cbind(b, min.cl.cor,
k, markers(hbs, k)))
colnames(snpis) <- c("chr", "corr", "clust",
"pos")
ids <- snpis[, .(chr, corr, clust)]
taggis <- apply(ids, 1, paste, collapse = "_")
snpis[, `:=`(tag, taggis)]
cluster.snps <- rbind(cluster.snps, snpis)
}
}
}
}
ids <- cluster.snps[, .(chr, corr, clust)]
taggis <- apply(ids, 1, paste, collapse = "_")
cluster.snps[, `:=`(tag, taggis)]
cluster.snps.sub <- cluster.snps[corr == min.cl.cor]
check.cluster.snps.sub <- cluster.snps.sub[,
.N, by = chr]
if (nrow(check.cluster.snps.sub) > 1) {
cluster.snps.sub[, `:=`(pos, as.numeric(pos))]
cluster.ord <- cluster.snps.sub[order(as.numeric(pos)),
.SD, by = .(chr, corr)]
ids <- cluster.ord[, .(chr, corr, clust)]
taggis <- apply(ids, 1, paste, collapse = "_")
cand.clust <- merge(cluster.ord, cands, by = c("chr",
"pos"))
afcolis <- grep("L[0-9]", colnames(cand.clust),
value = TRUE)
clust.tags.foc <- unique(cand.clust[chr ==
a, tag])
clust.tags <- unique(cand.clust[, tag])
combi.tab <- c()
corfoc <- c()
corcomp <- c()
print(paste("corr ", min.cl.cor))
for (i in clust.tags.foc) {
cat(".")
cluster.sub.i.raw <- cand.clust[tag == i,
]
if (nrow(cluster.sub.i.raw) > takerandom) {
red.pos <- sample(cluster.sub.i.raw$pos,
takerandom)
pos.indi <- cluster.sub.i.raw$pos %in%
red.pos
cluster.sub.i <- cluster.sub.i.raw[pos.indi,
]
}
else cluster.sub.i <- cluster.sub.i.raw
cluster.af.i <- cluster.sub.i[, afcolis,
with = FALSE]
cluster.scale.i <- transform.af(cluster.af.i)
minmax <- cand.clust[tag == i, .(min(pos),
max(pos))]
left <- na.exclude(unique(c(rev(cand.clust[chr ==
a & pos >= minmax[, V1] - winsize & pos <
minmax[, V2], tag]))))
right <- na.exclude(unique(cand.clust[chr ==
a & pos > minmax[, V2] & pos <= minmax[,
V2] + winsize, tag]))
neighbours <- unique(c(left[left != i], right))
if (length(neighbours) > 0) {
combi.tab.sub <- cbind(i, neighbours)
if (length(combi.tab) > 0) {
check1 <- combi.tab.sub[, 1] %in% combi.tab[,
2]
check2 <- combi.tab.sub[, 2] %in% combi.tab[,
1]
red.indi <- apply(cbind(check1, check2),
1, all)
combis <- combi.tab.sub[red.indi == F,
"neighbours"]
combi.tab <- rbind(combi.tab, combi.tab.sub[red.indi ==
F, ])
}
else {
combis <- combi.tab.sub[, "neighbours"]
combi.tab <- rbind(combi.tab, combi.tab.sub)
}
}
else {
combis <- c()
}
compare.raw <- clust.tags[grep(paste("^",
a, sep = ""), clust.tags, invert = T)]
compare.clust <- c(combis, compare.raw)
comclust <- function(x) {
sub <- cand.clust[tag == x, ]
if (nrow(sub) > takerandom) {
red.pos <- sample(sub$pos, takerandom)
pos.indi <- sub$pos %in% red.pos
sub <- sub[pos.indi, ]
}
cluster.af <- sub[, afcolis, with = FALSE]
cluster.scale <- transform.af(cluster.af)
clustcor.sub <- median(cor(cluster.scale.i,
cluster.scale), na.rm = TRUE)
return(clustcor.sub)
}
clustcor <- sapply(compare.clust, comclust)
corfoc <- c(corfoc, clustcor[compare.clust %in%
combis])
corcomp <- c(corcomp, clustcor[compare.clust %in%
combis == F])
}
}
if (length(corfoc) > 3 & length(corcomp) > 3) {
t <- t.test(fisherz(corfoc), fisherz(corcomp),
"greater")
p <- t$p.value
print(paste(" pval ", round(p, 4), " for chr ",
a, sep = ""))
if (p <= thres.ttest) {
old.cl.cor <- min.cl.cor
if (raw & search == F) {
min.cl.cor <- min.cl.cor - rawsteps
noT <- F
}
else {
min.cl.cor <- min.cl.cor - finesteps
raw <- FALSE
noT <- F
}
}
else {
if (raw & noT == F) {
raw <- F
min.cl.cor <- min.cl.cor + rawsteps - finesteps
}
else {
if (raw == F & noT == F) {
print("Normal")
clusteron <- FALSE
successful <- TRUE
cluster.final.sub <- cluster.snps[corr ==
old.cl.cor & chr == a]
final <- rbind(final, cluster.final.sub)
print(paste("chr ", a, " done", sep = ""))
}
}
if (raw & noT) {
min.cl.cor <- min.cl.cor + finesteps
search <- T
}
}
}
else {
if (search) {
print("search")
clusteron <- FALSE
successful <- TRUE
final.raw <- cluster.snps[corr == old.cl.cor &
chr == a, `:=`(tag, paste(tag, "_search",
sep = ""))]
final <- rbind(final, final.raw)
print(paste("chr ", a, " done", sep = ""))
}
else {
if (noT)
min.cl.cor <- min.cl.cor + finesteps
else min.cl.cor <- min.cl.cor - finesteps
}
}
if (min.cl.cor < 0.1 | min.cl.cor > 1) {
clusteron <- FALSE
successful <- TRUE
final.raw <- cluster.snps[corr == old.cl.cor &
chr == a, `:=`(tag, paste(tag, "_no_conv",
sep = ""))]
final <- rbind(final, final.raw[chr == a])
print(paste("chr ", a, " done; no convergence!",
sep = ""))
}
}
}
}
final[, `:=`(pos, as.numeric(pos))]
hapval.result[["all_haplotypes"]] <- final
red <- list()
if (final != "clustering impossible") {
corris <- unique(final[, .(chr, corr)])[, corr]
finish <- unique(str_extract(final[, tag], "no_.*"))
if (length(finish) == 1)
finish <- rep(finish, 2)
cands.cmh <- merge(cands, cmh, by = c("chr", "pos"))
datcmh <- merge(final, cands.cmh, by = c("chr", "pos"),
all = T)
datcmh.ord <- datcmh[order(score, decreasing = T)]
for (i in na.exclude(unique(datcmh.ord$tag))) {
x.sub <- final[tag == i]
minmax <- x.sub[, .(min(pos), max(pos))]
chr.sub <- unique(x.sub$chr)
maxtag <- datcmh.ord[pos >= minmax[, V1] - filterrange &
pos <= minmax[, V2] + filterrange & chr == chr.sub &
is.na(tag) == F, tag[which.max(score)]]
if (i != maxtag)
datcmh.ord[tag == i, `:=`(tag, NA)]
}
mergomat <- datcmh.ord[, .(chr, pos, score, tag)]
mergomat.final <- mergomat[is.na(tag) == F]
hapval.result[["dominant_haplotypes"]] <- mergomat.final
}
return(hapval.result)
}
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