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\name{est.map}
\alias{est.map}
\title{Estimate genetic maps}
\description{
Uses the Lander-Green algorithm (i.e., the hidden Markov model
technology) to re-estimate the genetic map for an experimental cross.
}
\usage{
est.map(cross, error.prob=0.0001,
map.function=c("haldane","kosambi","c-f","morgan"),
m=0, p=0, maxit=10000, tol=1e-6, sex.sp=TRUE,
verbose=FALSE, omit.noninformative=TRUE)
}
\arguments{
\item{cross}{An object of class \code{cross}. See
\code{\link[qtl]{read.cross}} for details.}
\item{error.prob}{Assumed genotyping error rate used in the calculation
of the penetrance Pr(observed genotype | true genotype).}
\item{map.function}{Indicates whether to use the Haldane, Kosambi,
Carter-Falconer, or Morgan map function when converting genetic
distances into recombination fractions. (Ignored if m > 0.)}
\item{m}{Interference parameter for the chi-square model for
interference; a non-negative integer, with m=0 corresponding to no
interference. This may be used only for a backcross or intercross.}
\item{p}{Proportion of chiasmata from the NI mechanism, in the Stahl
model; p=0 gives a pure chi-square model. This may be used only for
a backcross or intercross.}
\item{maxit}{Maximum number of EM iterations to perform.}
\item{tol}{Tolerance for determining convergence.}
\item{sex.sp}{Indicates whether to estimate sex-specific maps; this is
used only for the 4-way cross.}
\item{verbose}{Logical; indicates whether to print initial and final
estimates of the recombination fractions for each chromosome.}
\item{omit.noninformative}{If TRUE, on each chromosome, omit individuals
with fewer than two typed markers, since they are not informative for
linkage.}
}
\details{
By default, the map is estimated assuming no crossover interference,
but a map function is used to derive the genetic distances (though, by
default, the Haldane map function is used).
For a backcross or intercross, inter-marker distances may be estimated
using the Stahl model for crossover interference, of which the
chi-square model is a special case.
In the chi-square model, points are tossed down onto the four-strand
bundle according to a Poisson process, and every \eqn{(m+1)}st point is a
chiasma. With the assumption of no chromatid interference, crossover
locations on a random meiotic product are obtained by thinning the
chiasma process. The parameter \eqn{m} (a non-negative integer)
governs the strength of crossover interference, with \eqn{m=0}
corresponding to no interference.
In the Stahl model, chiasmata on the four-strand bundle are a
superposition of chiasmata from two mechanisms, one following a
chi-square model and one exhibiting no interference. An additional
parameter, \eqn{p}, gives the proportion of chiasmata from the no
interference mechanism.
}
\value{
A \code{map} object; a list whose components (corresponding to
chromosomes) are either vectors of marker positions (in cM) or
matrices with two rows of sex-specific marker positions.
The maximized log likelihood for each chromosome is saved as an
attribute named \code{loglik}. In the case that estimation was under
an interference model (with m > 0), allowed only for a backcross, m
and p are also included as attributes.
}
\author{Karl W Broman, \email{kbroman@biostat.wisc.edu} }
\examples{
data(fake.f2)
\dontshow{fake.f2 <- subset(fake.f2,chr=18:19)}
newmap <- est.map(fake.f2)
logliks <- sapply(newmap, attr, "loglik")
plot.map(fake.f2, newmap)
fake.f2 <- replace.map(fake.f2, newmap)
}
\references{
Armstrong, N. J., McPeek, M. J. and Speed, T. P. (2006) Incorporating
interference into linkage analysis for experimental crosses.
\emph{Biostatistics} \bold{7}, 374--386.
Lander, E. S. and Green, P. (1987) Construction of multilocus genetic linkage
maps in humans. \emph{Proc. Natl. Acad. Sci. USA} \bold{84}, 2363--2367.
Lange, K. (1999) \emph{Numerical analysis for statisticians}.
Springer-Verlag. Sec 23.3.
Rabiner, L. R. (1989) A tutorial on hidden Markov models and selected
applications in speech recognition. \emph{Proceedings of the IEEE}
\bold{77}, 257--286.
Zhao, H., Speed, T. P. and McPeek, M. S. (1995) Statistical analysis of
crossover interference using the chi-square model. \emph{Genetics}
\bold{139}, 1045--1056.
}
\seealso{ \code{\link[qtl]{plot.map}}, \code{\link[qtl]{replace.map}},
\code{\link[qtl]{est.rf}}, \code{\link[qtl]{fitstahl}} }
\keyword{utilities}
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