#' @title Association mapping on the X chromosome with poisson dist.
#' @author Elijah F Edmondson, \email{elijah.edmondson@@gmail.com}
#'
#' Performs association mapping in multiparent mouse populations.
#' @export
GRSDpoisson.xchr = function(obj, pheno, pheno.col, addcovar, tx, sanger.dir = "~/Desktop/R/QTL/WD/HS.sanger.files/") {
chr = obj$markers[1,2]
setwd(outdir)
file.prefix = paste(tx, pheno.col, "poisson", sep = "_")
plot.title = paste(tx, pheno.col, sep = " ")
strains = sub("/", "_", hs.colors[,2])
load(file = paste0(sanger.dir, "X.Rdata"))
#null.mod = glm(pheno[,pheno.col] ~ addcovar, family = binomial(logit))
null.mod = glm(pheno[,pheno.col] ~ addcovar, family = poisson(link = "log"))
null.ll = logLik(null.mod)
pv = rep(0, nrow(sanger))
glm.fxn = function(snp.rng, local.probs) {
sdp.nums = sanger[snp.rng,] %*% 2^(7:0)
sdps2keep = which(!duplicated(sdp.nums))
cur.sdps = sanger[snp.rng,,drop = FALSE][sdps2keep,,drop = FALSE]
unique.sdp.nums = sdp.nums[sdps2keep]
m = match(sdp.nums, unique.sdp.nums)
# Multiply the SDPs by the haplotype probabilities.
cur.alleles = tcrossprod(cur.sdps, local.probs)
cur.ll = rep(null.ll, nrow(cur.sdps))
# Check for low allele frequencies and remove SDPs with too
# few samples carrying one allele.
sdps.to.use = which(rowSums(cur.alleles) > 1.0)
sex.col = which(colnames(addcovar) == "sex")
if(length(sex.col) != 1) {
stop("One of the columns of addcovar MUST be named 'sex'.")
} # if(length(sex.col) != 1)
# Run the model at each unique SDP.
for(j in sdps.to.use) {
#full.mod = glm(pheno[,pheno.col] ~ addcovar + cur.alleles[j,], family = binomial(logit))
full.mod = glm(pheno[,pheno.col] ~ addcovar + cur.alleles[j,], family = poisson(link = "log"))
cur.ll[j] = logLik(full.mod)
} # for(j)
# This is the LRS.
cur.ll = cur.ll - null.ll
# Return the results.
cur.ll[m]
} # glm.fxn()
# SNPs before the first marker.
snp.rng = which(sanger.hdr$POS <= obj$markers[1,3])
if(length(snp.rng) > 0) {
pv[snp.rng] = glm.fxn(snp.rng, obj$probs[,,1])
} # if(length(snp.rng) > 0)
# SNPs between Markers.
for(i in 1:(nrow(obj$markers)-1)) {
snp.rng = which(sanger.hdr$POS > obj$markers[i,3] &
sanger.hdr$POS <= obj$markers[i+1,3])
if(length(snp.rng) > 0) {
# Take the mean of the haplotype probs at the surrounding markers.
pv[snp.rng] = glm.fxn(snp.rng, (obj$probs[,,i] +
obj$probs[,,i+1]) * 0.5)
} # if(length(snp.rng) > 0)
} # for(i)
# SNPs after the last marker.
snp.rng = which(sanger.hdr$POS > obj$markers[nrow(obj$markers),3])
if(length(snp.rng) > 0) {
pv[snp.rng] = glm.fxn(snp.rng, obj$probs[,,nrow(obj$markers)])
} # if(length(snp.rng) > 0)
# Convert LRS to p-values using the chi-squared distribution.
pv = pchisq(1 * pv, df = 4, lower.tail = FALSE)
pv = data.frame(sanger.hdr, pv, stringsAsFactors = FALSE)
save(pv, file = paste0(file.prefix, "_chr", chr, ".Rdata"))
png(paste0(file.prefix, "_chr", chr,".png"), width = 2600,
height = 1600, res = 200)
plot(as.numeric(pv[,3]) * 1e-6, -log10(pv[,6]), pch = 20)
mtext(side = 3, line = 0.5, text = paste(plot.title, ": Chr", chr))
dev.off()
# Return the positions and p-values.
return(pv)
} # GRSDpoisson.xchr()
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