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\name{effectscan}
\alias{effectscan}
\title{Plot estimated QTL effects across the whole genome}
\description{
This function is used to plot the estimated QTL effects along
selected chromosomes. For a backcross, there will be only one line,
representing the additive effect. For an intercross, there will be two
lines, representing the additive and dominance effects.
}
\usage{
effectscan(cross, pheno.col=1, chr, get.se=FALSE, draw=TRUE,
gap=25, ylim, mtick=c("line","triangle"),
add.legend=TRUE, alternate.chrid=FALSE, \dots)
}
\arguments{
\item{cross}{An object of class \code{cross}.}
\item{pheno.col}{Column number in the phenotype matrix which to be
drawn in the plot. One may also give a character string matching
a phenotype name.}
\item{chr}{Chromosome(s) to be drawn in the plot (optional).}
\item{get.se}{If TRUE, estimated standard errors are calculated.}
\item{draw}{If TRUE, draw the figure.}
\item{gap}{Gap separating chromosomes (in cM).}
\item{ylim}{Y-axis limits (optional).}
\item{mtick}{Tick mark type for markers.}
\item{add.legend}{If TRUE, add a legend.}
\item{alternate.chrid}{If TRUE and more than one chromosome is
plotted, alternate the placement of chromosome
axis labels, so that they may be more easily distinguished.}
\item{\dots}{Passed to the function \code{\link[graphics]{plot}} when it
is called.}
}
\value{
The results are returned silently, as an object of class
\code{"effectscan"}, which is the same as the form returned by the
function \code{\link[qtl]{scanone}}, though with estimated effects
where LOD scores might be. That is, it is a data frame with the first
two columns being chromosome ID and position (in cM), and subsequent
columns being estimated effects, and (if \code{get.se=TRUE}) standard
errors.
}
\details{
The results of \code{\link[qtl]{sim.geno}} are required for taking
account of missing genotype information.
For a backcross, the additive effect is estimated as the difference
between the phenotypic averages for heterozygotes and homozygotes.
For recombinant inbred lines, the additive effect is estimated as half
the difference between the phenotypic averages for the two homozygotes.
For an intercross, the additive and dominance effects are estimated
from linear regression on \eqn{a} and \eqn{d} with \eqn{a} = -1, 0, 1,
for the AA, AB and BB genotypes, respectively, and \eqn{d} = 0, 1, 0,
for the AA, AB and BB genotypes, respectively.
As usual, the X chromosome is a bit more complicated. We estimate
separate additive effects for the two sexes, and for the two
directions within females.
There is a function \code{\link[qtl]{plot.effectscan}} that creates
the actual plot by calling \code{plot.scanone}. In the case
\code{get.se=TRUE}, colored regions indicate \eqn{\pm}{+/-} 1 SE.
}
\examples{
data(fake.f2)
\dontshow{fake.f2 <- subset(fake.f2, chr=c(1,13,"X"))}
fake.f2 <- sim.geno(fake.f2, step=2.5, n.draws=16)
# allelic effect on whole genome
effectscan(fake.f2)
# on chromosome 13, include standard errors
effectscan(fake.f2, chr="13", mtick="triangle", get.se=TRUE)
}
\references{
Sen, \'S. and Churchill, G. A. (2001) A statistical framework for quantitative
trait mapping. \emph{Genetics} \bold{159}, 371--387.
}
\author{Karl W. Broman, \email{kbroman@biostat.wisc.edu}}
\seealso{ \code{\link[qtl]{effectplot}}, \code{\link[qtl]{plot.pxg}},
\code{\link[qtl]{sim.geno}} }
\keyword{hplot}
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