EVOLUTION-MANAGER
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<!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: Composite interval mapping</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 cim {qtl}"><tr><td>cim {qtl}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Composite interval mapping</h2> <h3>Description</h3> <p>Composite interval mapping by a scheme from QTL Cartographer: forward selection at the markers (here, with filled-in genotype data) to a fixed number, followed by interval mapping with the selected markers as covariates, dropping marker covariates if they are within some fixed window size of the location under test. </p> <h3>Usage</h3> <pre> cim(cross, pheno.col=1, n.marcovar=3, window=10, method=c("em", "imp", "hk", "ehk"), imp.method=c("imp", "argmax"), error.prob=0.0001, map.function=c("haldane", "kosambi", "c-v", "morgan"), addcovar=NULL, n.perm) </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>pheno.col</code></td> <td> <p>Column number in the phenotype matrix which should be used as the phenotype. One may also give a character string matching a phenotype name. Finally, one may give a numeric vector of phenotypes, in which case it must have the length equal to the number of individuals in the cross, and there must be either non-integers or values < 1 or > no. phenotypes; this last case may be useful for studying transformations.</p> </td></tr> <tr valign="top"><td><code>n.marcovar</code></td> <td> <p>Number of marker covariates to use.</p> </td></tr> <tr valign="top"><td><code>window</code></td> <td> <p>Window size, in cM.</p> </td></tr> <tr valign="top"><td><code>method</code></td> <td> <p>Indicates whether to use the EM algorithm, imputation, Haley-Knott regression, or the extended Haley-Knott method.</p> </td></tr> <tr valign="top"><td><code>imp.method</code></td> <td> <p>Method used to impute any missing marker genotype data.</p> </td></tr> <tr valign="top"><td><code>error.prob</code></td> <td> <p>Genotyping error probability assumed when imputing the missing marker genotype data.</p> </td></tr> <tr valign="top"><td><code>map.function</code></td> <td> <p>Map function used when imputing the missing marker genotype data.</p> </td></tr> <tr valign="top"><td><code>addcovar</code></td> <td> <p>Optional numeric matrix of additional covariates to include.</p> </td></tr> <tr valign="top"><td><code>n.perm</code></td> <td> <p>If specified, a permutation test is performed rather than an analysis of the observed data. This argument defines the number of permutation replicates.</p> </td></tr> </table> <h3>Details</h3> <p>We first use <code><a href="fill.geno.html">fill.geno</a></code> to impute any missing marker genotype data, either via a simple random imputation or using the Viterbi algorithm. </p> <p>We then perform forward selection to a fixed number of markers. These will be used (again, with any missing data filled in) as covariates in the subsequent genome scan. </p> <h3>Value</h3> <p>The function returns an object of the same form as the function <code><a href="scanone.html">scanone</a></code>: </p> <p>If <code>n.perm</code> is missing, the function returns the scan results as a data.frame with three columns: chromosome, position, LOD score. Attributes indicate the names and positions of the chosen marker covariates. </p> <p>If <code>n.perm</code> > 0, the function results the results of a permutation test: a vector giving the genome-wide maximum LOD score in each of the permutations. </p> <h3>Author(s)</h3> <p>Karl W Broman, <a href="mailto:broman@wisc.edu">broman@wisc.edu</a></p> <h3>References</h3> <p>Jansen, R. C. (1993) Interval mapping of multiple quantitative trait loci. <em>Genetics</em>, <b>135</b>, 205–211. </p> <p>Jansen, R. C. and Stam, P. (1994) High resolution of quantitative traits into multiple loci via interval mapping. <em>Genetics</em>, <b>136</b>, 1447-1455. </p> <p>Zeng, Z. B. (1993) Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. <em>Proc. Natl. Acad. Sci. USA</em>, <b>90</b>, 10972–10976. </p> <p>Zeng, Z. B. (1994) Precision mapping of quantitative trait loci. <em>Genetics</em>, <b>136</b>, 1457–1468. </p> <h3>See Also</h3> <p><code><a href="add.cim.covar.html">add.cim.covar</a></code>, <code><a href="scanone.html">scanone</a></code>, <code><a href="summary.scanone.html">summary.scanone</a></code>, <code><a href="plot.scanone.html">plot.scanone</a></code>, <code><a href="fill.geno.html">fill.geno</a></code></p> <h3>Examples</h3> <pre> data(hyper) hyper <- calc.genoprob(hyper, step=2.5) out <- scanone(hyper) out.cim <- cim(hyper, n.marcovar=3) plot(out, out.cim, chr=c(1,4,6,15), col=c("blue", "red")) add.cim.covar(out.cim, chr=c(1,4,6,15)) </pre> <hr /><div style="text-align: center;">[Package <em>qtl</em> version 1.66 <a href="00Index.html">Index</a>]</div> </body></html>