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: Reconstruct underlying genotypes</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 argmax.geno {qtl}"><tr><td>argmax.geno {qtl}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Reconstruct underlying genotypes</h2> <h3>Description</h3> <p>Uses the Viterbi algorithm to identify the most likely sequence of underlying genotypes, given the observed multipoint marker data, with possible allowance for genotyping errors. </p> <h3>Usage</h3> <pre> argmax.geno(cross, step=0, off.end=0, error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), stepwidth=c("fixed", "variable", "max")) </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>step</code></td> <td> <p>Maximum distance (in cM) between positions at which the genotypes are reconstructed, though for <code>step=0</code>, genotypes are reconstructed only at the marker locations.</p> </td></tr> <tr valign="top"><td><code>off.end</code></td> <td> <p>Distance (in cM) past the terminal markers on each chromosome to which the genotype reconstructions will be carried.</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.</p> </td></tr> <tr valign="top"><td><code>stepwidth</code></td> <td> <p>Indicates whether the intermediate points should with fixed or variable step sizes. We recommend using <code>"fixed"</code>; <code>"variable"</code> was included for the qtlbim package (<a href="https://cran.r-project.org/src/contrib/Archive/qtlbim">https://cran.r-project.org/src/contrib/Archive/qtlbim</a>). The <code>"max"</code> option inserts the minimal number of intermediate points so that the maximum distance between points is <code>step</code>.</p> </td></tr> </table> <h3>Details</h3> <p>We use the Viterbi algorithm to calculate <i>arg max_v Pr(g = v | O)</i> where <i>g</i> is the underlying sequence of genotypes and <i>O</i> is the observed marker genotypes. </p> <p>This is done by calculating <i> Q[k](v[k]) = max{v[1], …, v[k-1]} Pr(g[1] = v[1], …, g[k] = v[k], O[1], …, O[k])</i> for <i>k = 1, …, n</i> and then tracing back through the sequence. </p> <h3>Value</h3> <p>The input <code>cross</code> object is returned with a component, <code>argmax</code>, added to each component of <code>cross$geno</code>. The <code>argmax</code> component is a matrix of size [n.ind x n.pos], where n.pos is the number of positions at which the reconstructed genotypes were obtained, containing the most likely sequences of underlying genotypes. Attributes <code>"error.prob"</code>, <code>"step"</code>, and <code>"off.end"</code> are set to the values of the corresponding arguments, for later reference. </p> <h3>Warning</h3> <p>The Viterbi algorithm can behave badly when <code>step</code> is small but positive. One may observe quite different results for different values of <code>step</code>. </p> <p>The problem is that, in the presence of data like <code>A----H</code>, the sequences <code>AAAAAA</code> and <code>HHHHHH</code> may be more likely than any one of the sequences <code>AAAAAH</code>, <code>AAAAHH</code>, <code>AAAHHH</code>, <code>AAHHHH</code>, <code>AHHHHH</code>, <code>AAAAAH</code>. The Viterbi algorithm produces a single "most likely" sequence of underlying genotypes. </p> <h3>Author(s)</h3> <p>Karl W Broman, <a href="mailto:broman@wisc.edu">broman@wisc.edu</a> </p> <h3>References</h3> <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> <h3>See Also</h3> <p><code><a href="sim.geno.html">sim.geno</a></code>, <code><a href="calc.genoprob.html">calc.genoprob</a></code>, <code><a href="fill.geno.html">fill.geno</a></code> </p> <h3>Examples</h3> <pre> data(fake.f2) fake.f2 <- argmax.geno(fake.f2, step=2, off.end=5, err=0.01) </pre> <hr /><div style="text-align: center;">[Package <em>qtl</em> version 1.46-2 <a href="00Index.html">Index</a>]</div> </body></html>