EVOLUTION-MANAGER
Edit File: est.map.html
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Estimate genetic maps</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body> <table width="100%" summary="page for est.map {qtl}"><tr><td>est.map {qtl}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Estimate genetic maps</h2> <h3>Description</h3> <p>Uses the Lander-Green algorithm (i.e., the hidden Markov model technology) to re-estimate the genetic map for an experimental cross. </p> <h3>Usage</h3> <pre> 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) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>cross</code></td> <td> <p>An object of class <code>cross</code>. See <code><a href="read.cross.html">read.cross</a></code> for details.</p> </td></tr> <tr valign="top"><td><code>chr</code></td> <td> <p>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 <code>-</code> to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used.</p> </td></tr> <tr valign="top"><td><code>error.prob</code></td> <td> <p>Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype).</p> </td></tr> <tr valign="top"><td><code>map.function</code></td> <td> <p>Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. (Ignored if m > 0.)</p> </td></tr> <tr valign="top"><td><code>m</code></td> <td> <p>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.</p> </td></tr> <tr valign="top"><td><code>p</code></td> <td> <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.</p> </td></tr> <tr valign="top"><td><code>maxit</code></td> <td> <p>Maximum number of EM iterations to perform.</p> </td></tr> <tr valign="top"><td><code>tol</code></td> <td> <p>Tolerance for determining convergence.</p> </td></tr> <tr valign="top"><td><code>sex.sp</code></td> <td> <p>Indicates whether to estimate sex-specific maps; this is used only for the 4-way cross.</p> </td></tr> <tr valign="top"><td><code>verbose</code></td> <td> <p>If TRUE, print tracing information.</p> </td></tr> <tr valign="top"><td><code>omit.noninformative</code></td> <td> <p>If TRUE, on each chromosome, omit individuals with fewer than two typed markers, since they are not informative for linkage.</p> </td></tr> <tr valign="top"><td><code>offset</code></td> <td> <p>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.</p> </td></tr> <tr valign="top"><td><code>n.cluster</code></td> <td> <p>If the package <code>snow</code> is available calculations for multiple chromosomes are run in parallel using this number of nodes.</p> </td></tr> </table> <h3>Details</h3> <p>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). </p> <p>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. </p> <p>In the chi-square model, points are tossed down onto the four-strand bundle according to a Poisson process, and every <i>(m+1)</i>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 <i>m</i> (a non-negative integer) governs the strength of crossover interference, with <i>m=0</i> corresponding to no interference. </p> <p>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, <i>p</i>, gives the proportion of chiasmata from the no interference mechanism. </p> <h3>Value</h3> <p>A <code>map</code> 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</code>. 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. </p> <h3>Author(s)</h3> <p>Karl W Broman, <a href="mailto:broman@wisc.edu">broman@wisc.edu</a> </p> <h3>References</h3> <p>Armstrong, N. J., McPeek, M. J. and Speed, T. P. (2006) Incorporating interference into linkage analysis for experimental crosses. <em>Biostatistics</em> <b>7</b>, 374–386. </p> <p>Lander, E. S. and Green, P. (1987) Construction of multilocus genetic linkage maps in humans. <em>Proc. Natl. Acad. Sci. USA</em> <b>84</b>, 2363–2367. </p> <p>Lange, K. (1999) <em>Numerical analysis for statisticians</em>. Springer-Verlag. Sec 23.3. </p> <p>Rabiner, L. R. (1989) A tutorial on hidden Markov models and selected applications in speech recognition. <em>Proceedings of the IEEE</em> <b>77</b>, 257–286. </p> <p>Zhao, H., Speed, T. P. and McPeek, M. S. (1995) Statistical analysis of crossover interference using the chi-square model. <em>Genetics</em> <b>139</b>, 1045–1056. </p> <h3>See Also</h3> <p><code><a href="map2table.html">map2table</a></code>, <code><a href="plot.map.html">plotMap</a></code>, <code><a href="replace.map.html">replace.map</a></code>, <code><a href="est.rf.html">est.rf</a></code>, <code><a href="fitstahl.html">fitstahl</a></code> </p> <h3>Examples</h3> <pre> data(fake.f2) newmap <- est.map(fake.f2) logliks <- sapply(newmap, attr, "loglik") plotMap(fake.f2, newmap) fake.f2 <- replace.map(fake.f2, newmap) </pre> <hr /><div style="text-align: center;">[Package <em>qtl</em> version 1.66 <a href="00Index.html">Index</a>]</div> </body></html>