EVOLUTION-MANAGER
Edit File: effectscan.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: Plot estimated QTL effects across the whole genome</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 effectscan {qtl}"><tr><td>effectscan {qtl}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Plot estimated QTL effects across the whole genome</h2> <h3>Description</h3> <p>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. </p> <h3>Usage</h3> <pre> effectscan(cross, pheno.col=1, chr, get.se=FALSE, draw=TRUE, gap=25, ylim, mtick=c("line","triangle"), add.legend=TRUE, alternate.chrid=FALSE, ...) </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>.</p> </td></tr> <tr valign="top"><td><code>pheno.col</code></td> <td> <p>Column number in the phenotype matrix which to be drawn in the plot. One may also give a character string matching a phenotype name.</p> </td></tr> <tr valign="top"><td><code>chr</code></td> <td> <p>Optional vector indicating the chromosomes to be drawn in the plot. 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>get.se</code></td> <td> <p>If TRUE, estimated standard errors are calculated.</p> </td></tr> <tr valign="top"><td><code>draw</code></td> <td> <p>If TRUE, draw the figure.</p> </td></tr> <tr valign="top"><td><code>gap</code></td> <td> <p>Gap separating chromosomes (in cM).</p> </td></tr> <tr valign="top"><td><code>ylim</code></td> <td> <p>Y-axis limits (optional).</p> </td></tr> <tr valign="top"><td><code>mtick</code></td> <td> <p>Tick mark type for markers.</p> </td></tr> <tr valign="top"><td><code>add.legend</code></td> <td> <p>If TRUE, add a legend.</p> </td></tr> <tr valign="top"><td><code>alternate.chrid</code></td> <td> <p>If TRUE and more than one chromosome is plotted, alternate the placement of chromosome axis labels, so that they may be more easily distinguished.</p> </td></tr> <tr valign="top"><td><code>...</code></td> <td> <p>Passed to the function <code>plot</code> when it is called.</p> </td></tr> </table> <h3>Details</h3> <p>The results of <code><a href="sim.geno.html">sim.geno</a></code> are required for taking account of missing genotype information. </p> <p>For a backcross, the additive effect is estimated as the difference between the phenotypic averages for heterozygotes and homozygotes. </p> <p>For recombinant inbred lines, the additive effect is estimated as half the difference between the phenotypic averages for the two homozygotes. </p> <p>For an intercross, the additive and dominance effects are estimated from linear regression on <i>a</i> and <i>d</i> with <i>a</i> = -1, 0, 1, for the AA, AB and BB genotypes, respectively, and <i>d</i> = 0, 1, 0, for the AA, AB and BB genotypes, respectively. </p> <p>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. </p> <p>There is an internal function <code>plot.effectscan</code> that creates the actual plot by calling <code>plot.scanone</code>. In the case <code>get.se=TRUE</code>, colored regions indicate <i>+/-</i> 1 SE. </p> <h3>Value</h3> <p>The results are returned silently, as an object of class <code>"effectscan"</code>, which is the same as the form returned by the function <code><a href="scanone.html">scanone</a></code>, 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</code>) standard errors. </p> <h3>Author(s)</h3> <p>Karl W. Broman, <a href="mailto:broman@wisc.edu">broman@wisc.edu</a></p> <h3>References</h3> <p>Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. <em>Genetics</em> <b>159</b>, 371–387. </p> <h3>See Also</h3> <p><code><a href="effectplot.html">effectplot</a></code>, <code><a href="plot.pxg.html">plotPXG</a></code>, <code><a href="sim.geno.html">sim.geno</a></code> </p> <h3>Examples</h3> <pre> data(fake.f2) 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) </pre> <hr /><div style="text-align: center;">[Package <em>qtl</em> version 1.66 <a href="00Index.html">Index</a>]</div> </body></html>