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######################################################################
#
# discan.R
#
# copyright (c) 2001-2015, Karl W Broman
# last modified Oct, 2015
# first written Oct, 2001
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License,
# version 3, as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but without any warranty; without even the implied warranty of
# merchantability or fitness for a particular purpose. See the GNU
# General Public License, version 3, for more details.
#
# A copy of the GNU General Public License, version 3, is available
# at http://www.r-project.org/Licenses/GPL-3
#
# Part of the R/qtl package
# Contains: discan
#
######################################################################
######################################################################
#
# discan: scan genome, calculating LOD scores with single QTL model
# for a dichotomous trait
#
######################################################################
discan <-
function(cross, pheno, method=c("em","hk","mr"),
addcovar=NULL, intcovar=NULL, maxit=4000, tol=1e-4,
verbose=FALSE, give.warnings=TRUE, ind.noqtl)
{
method <- match.arg(method)
n.ind <- nind(cross)
n.chr <- nchr(cross)
type <- class(cross)[1]
if(is.null(addcovar)) n.addcovar <- 0
else n.addcovar <- ncol(addcovar)
if(is.null(intcovar)) n.intcovar <- 0
else n.intcovar <- ncol(intcovar)
# individuals with no QTL effect
if(missing(ind.noqtl)) ind.noqtl <- rep(FALSE, nind(cross))
else {
if(!is.logical(ind.noqtl) || length(ind.noqtl) != nind(cross))
stop("ind.noqtl be a logical vector of length n.ind (", nind(cross), ")")
if(sum(ind.noqtl) > 1) {
if(method == "mr") {
ind.noqtl <- rep(FALSE, nind(cross))
warning("ind.noqtl ignored for method=", method, ", model=binary")
}
else if(is.null(addcovar) && (!is.logical(ind.noqtl) || any(ind.noqtl))) {
ind.noqtl <- rep(FALSE, nind(cross))
warning("ind.noqtl ignored when no additive covariates")
}
}
}
if(method=="mr" && n.addcovar+n.intcovar>0) {
if(give.warnings) warning("Covariates ignored with method=\"mr\"; use \"em\" instead")
n.addcovar <- n.intcovar <- addcovar <- intcovar <- 0
}
u <- unique(pheno)
if(any(u != 0 & u != 1))
stop("Phenotypes must be either 0 or 1.")
results <- NULL
llik0 <- c("A"=NA,"X"=NA)
nullcoef <- list("A"=NA,"X"=NA)
for(i in 1:n.chr) {
chrtype <- class(cross$geno[[i]])
if(chrtype=="X") {
sexpgm <- getsex(cross)
ac <- revisecovar(sexpgm,addcovar)
if(!is.null(addcovar) && (nd <- attr(ac, "n.dropped")) > 0 && give.warnings)
warning("Dropped ", nd, " additive covariates on X chromosome.")
if(length(ac)==0) {
n.ac <- 0
ac <- NULL
}
else n.ac <- ncol(ac)
ic <- revisecovar(sexpgm,intcovar)
if(!is.null(intcovar) && (nd <- attr(ic, "n.dropped")) > 0 && give.warnings)
warning("Dropped ", nd, " interactive covariates on X chromosome.")
if(length(ic)==0) {
n.ic <- 0
ic <- NULL
}
else n.ic <- ncol(ic)
}
else {
sexpgm <- NULL
ac <- addcovar
n.ac <- n.addcovar
ic <- intcovar
n.ic <- n.intcovar
}
# get null log liklihood
if(is.na(llik0[chrtype])) {
if(n.ac > 0)
nullfit <- glm(pheno ~ ac, family=binomial(link="logit"))
else if(i==1)
nullfit <- glm(pheno ~ 1, family=binomial(link="logit"))
fitted <- nullfit$fitted
nullcoef[[chrtype]] <- nullfit$coef
llik0[chrtype] <- sum(pheno*log10(fitted) + (1-pheno)*log10(1-fitted))
}
# get genotype names
gen.names <- getgenonames(type,chrtype,"full",sexpgm,attributes(cross))
n.gen <- length(gen.names)
# pull out genotype data (mr)
# or genotype probabilities (em)
if(method == "mr") {
newgeno <- cross$geno[[i]]$data
newgeno[is.na(newgeno)] <- 0
# discard partially informative genotypes
if(type=="f2") newgeno[newgeno>3] <- 0
if(type=="4way") newgeno[newgeno>4] <- 0
# revise X chromosome genotypes
if(chrtype=="X" && (type=="bc" || type=="f2"))
newgeno <- reviseXdata(type, "full", sexpgm, geno=newgeno,
cross.attr=attributes(cross))
n.pos <- ncol(newgeno)
map <- cross$geno[[i]]$map
if(is.matrix(map)) map <- map[1,]
z <- .C("R_discan_mr",
as.integer(n.ind), # number of individuals
as.integer(n.pos), # number of markers
as.integer(n.gen), # number of possible genotypes
as.integer(newgeno), # genotype data
as.integer(pheno), # phenotype data
result=as.double(rep(0,n.pos*(n.gen+1))),
PACKAGE="qtl")
}
else {
if(!("prob" %in% names(cross$geno[[i]]))) { # need to run calc.genoprob
if(give.warnings) warning("First running calc.genoprob.")
cross <- calc.genoprob(cross)
}
genoprob <- cross$geno[[i]]$prob
n.pos <- ncol(genoprob)
# revise X chromosome genotypes
if(chrtype=="X" && (type=="bc" || type=="f2"))
genoprob <- reviseXdata(type, "full", sexpgm, prob=genoprob,
cross.attr=attributes(cross))
if("map" %in% names(attributes(cross$geno[[i]]$prob)))
map <- attr(cross$geno[[i]]$prob,"map")
else {
stp <- attr(cross$geno[[i]]$prob, "step")
oe <- attr(cross$geno[[i]]$prob, "off.end")
if("stepwidth" %in% names(attributes(cross$geno[[i]]$prob)))
stpw <- attr(cross$geno[[i]]$prob, "stepwidth")
else stpw <- "fixed"
map <- create.map(cross$geno[[i]]$map,stp,oe,stpw)
}
if(is.matrix(map)) map <- map[1,]
if(method=="hk") {
z <- .C("R_scanone_hk_binary",
as.integer(n.ind), # number of individuals
as.integer(n.pos), # number of markers
as.integer(n.gen), # number of possible genotypes
as.double(genoprob), # genotype probabilities
as.double(ac),
as.integer(n.ac),
as.double(ic),
as.integer(n.ic),
as.double(pheno), # phenotype data
result=as.double(rep(0,n.pos)),
as.double(tol),
as.integer(maxit),
as.integer(verbose),
as.integer(ind.noqtl),
PACKAGE="qtl")
}
else if(n.ac + n.ic > 0) {
start <- rep(nullcoef[[chrtype]][1],n.gen)
if(n.ac > 0)
start <- c(start, nullcoef[[chrtype]][-1])
if(n.ic > 0)
start <- c(start, rep(0, n.ic*(n.gen-1)))
z <- .C("R_discan_covar",
as.integer(n.ind), # number of individuals
as.integer(n.pos), # number of markers
as.integer(n.gen), # number of possible genotypes
as.double(genoprob), # genotype probabilities
as.double(ac),
as.integer(n.ac),
as.double(ic),
as.integer(n.ic),
as.integer(pheno), # phenotype data
as.double(start),
result=as.double(rep(0,n.pos)),
as.integer(maxit),
as.double(tol),
as.integer(verbose),
as.integer(ind.noqtl),
PACKAGE="qtl")
}
else {
z <- .C("R_discan_im",
as.integer(n.ind), # number of individuals
as.integer(n.pos), # number of markers
as.integer(n.gen), # number of possible genotypes
as.double(genoprob), # genotype probabilities
as.integer(pheno), # phenotype data
result=as.double(rep(0,n.pos)),
as.integer(maxit),
as.double(tol),
PACKAGE="qtl")
}
}
z <- matrix(z$result,nrow=n.pos)
if(method != "mr") z[,1] <- z[,1] - llik0[chrtype]
z[is.na(z[,1]),1] <- 0
z <- z[,1,drop=FALSE]
colnames(z)[1] <- "lod"
# get null log10 likelihood for the X chromosome
adjustX <- FALSE
if(chrtype=="X") {
# determine which covariates belong in null hypothesis
temp <- scanoneXnull(type, sexpgm, cross.attr=attributes(cross))
adjustX <- temp$adjustX
parX0 <- temp$parX0+n.ac
sexpgmcovar <- temp$sexpgmcovar
sexpgmcovar.alt <- temp$sexpgmcovar.alt
if(adjustX) { # get LOD-score adjustment
if(n.ac > 0) {
nullfitX <- glm(pheno ~ ac+sexpgmcovar,
family=binomial(link="logit"))
parX0 <- lm(pheno~ac+sexpgmcovar)$rank
}
else
nullfitX <- glm(pheno ~ sexpgmcovar,
family=binomial(link="logit"))
fittedX <- nullfitX$fitted
llik0X <- sum(pheno*log10(fittedX) + (1-pheno)*log10(1-fittedX))
# adjust LOD curve
z <- z - (llik0X - llik0["X"])
}
}
w <- names(map)
o <- grep("^loc-*[0-9]+",w)
if(length(o) > 0) # inter-marker locations cited as "c*.loc*"
w[o] <- paste("c",names(cross$geno)[i],".",w[o],sep="")
z <- data.frame(chr=rep(names(cross$geno)[i],length(map)),
pos=as.numeric(map), z, stringsAsFactors=TRUE)
rownames(z) <- w
results <- rbind(results, z)
} # loop over chromosomes
class(results) <- c("scanone","data.frame")
attr(results,"method") <- method
attr(results,"type") <- type
attr(results,"model") <- "binary"
attr(results,"null.log10.lik") <- llik0["A"]
if(adjustX)
attr(results,"null.log10.lik.X") <- llik0X
results
}
# end of discan.R
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