Uses the Lander-Green algorithm (i.e., the hidden Markov model technology) to re-estimate the genetic map for an experimental cross.
est.map(cross, chr, 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, offset, n.cluster=1)A 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 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.
An object of class cross. See
read.cross for details.
Optional vector indicating the chromosomes to consider.
This should be a vector of character
strings referring to chromosomes by name; numeric values are
converted to strings. Refer to chromosomes with a preceding -
to have all chromosomes but those considered. A logical (TRUE/FALSE)
vector may also be used.
Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype).
Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. (Ignored if m > 0.)
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.
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.
Maximum number of EM iterations to perform.
Tolerance for determining convergence.
Indicates whether to estimate sex-specific maps; this is used only for the 4-way cross.
If TRUE, print tracing information.
If TRUE, on each chromosome, omit individuals with fewer than two typed markers, since they are not informative for linkage.
Defines the starting position for each chromosome. If missing, we use the starting positions that are currently present in the input cross object. This should be a single value (to be used for all chromosomes) or a vector with length equal to the number of chromosomes, defining individual starting positions for each chromosome. For a sex-specific map (as in a 4-way cross), we use the same offset for both the male and female maps.
If the package snow is available
calculations for multiple chromosomes are run in parallel using this
number of nodes.
Karl W Broman, broman@wisc.edu
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 \((m+1)\)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 \(m\) (a non-negative integer) governs the strength of crossover interference, with \(m=0\) 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, \(p\), gives the proportion of chiasmata from the no interference mechanism.
Armstrong, N. J., McPeek, M. J. and Speed, T. P. (2006) Incorporating interference into linkage analysis for experimental crosses. Biostatistics 7, 374--386.
Lander, E. S. and Green, P. (1987) Construction of multilocus genetic linkage maps in humans. Proc. Natl. Acad. Sci. USA 84, 2363--2367.
Lange, K. (1999) 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. Proceedings of the IEEE 77, 257--286.
Zhao, H., Speed, T. P. and McPeek, M. S. (1995) Statistical analysis of crossover interference using the chi-square model. Genetics 139, 1045--1056.
map2table, plotMap, replace.map,
est.rf, fitstahl
data(fake.f2)
fake.f2 <- subset(fake.f2,chr=18:19)
newmap <- est.map(fake.f2)
logliks <- sapply(newmap, attr, "loglik")
plotMap(fake.f2, newmap)
fake.f2 <- replace.map(fake.f2, newmap)
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