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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: Estimate FDR for multiple trait QTL analysis</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 mqmscanfdr {qtl}"><tr><td>mqmscanfdr {qtl}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2> Estimate FDR for multiple trait QTL analysis </h2> <h3>Description</h3> <p>Estimate the false discovery rate (FDR) for multiple trait analysis </p> <h3>Usage</h3> <pre> mqmscanfdr(cross, scanfunction=mqmscanall, thresholds=c(1,2,3,4,5,7,10,15,20), n.perm=10, verbose=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>. See <code><a href="read.cross.html">read.cross</a></code> for details. </p> </td></tr> <tr valign="top"><td><code>scanfunction</code></td> <td> <p> QTL mapping function, Note: Must use scanall or mqmscanall. Otherwise this will not produce usefull results. Reason: We need a function that maps all traits ecause of the correlation structure which is not changed (between traits) during permutation (Valis options: scanall or mqmscanall) </p> </td></tr> <tr valign="top"><td><code>thresholds</code></td> <td> <p> False discovery rate (FDR) is calculated for peaks above these LOD thresholds (DEFAULT=Range from 1 to 20, using 10 thresholds) Parameter is a list of LOD scores at which FDR is calculated. </p> </td></tr> <tr valign="top"><td><code>n.perm</code></td> <td> <p> Number of permutations (DEFAULT=10 for quick analysis, however for publications use 1000, or higher) </p> </td></tr> <tr valign="top"><td><code>verbose</code></td> <td> <p> verbose output </p> </td></tr> <tr valign="top"><td><code>...</code></td> <td> <p> Parameters passed to the mapping function </p> </td></tr> </table> <h3>Details</h3> <p>This function wraps the analysis of <code><a href="scanone.html">scanone</a></code>, <code><a href="cim.html">cim</a></code> and <code><a href="mqmscan.html">mqmscan</a></code> to scan for QTL in shuffled/randomized data. It is recommended to also install the <code>snow</code> library for parallelization of calculations. The <code>snow</code> library allows calculations to run on multiple cores or even scale it up to an entire cluster, thus speeding up calculation by the number of computers used. </p> <h3>Value</h3> <p>Returns a data.frame with 3 columns: FalsePositives, FalseNegatives and False Discovery Rates. In the rows the userspecified thresholds are with scores for the 3 columns. </p> <h3>Author(s)</h3> <p>Ritsert C Jansen; Danny Arends; Pjotr Prins; Karl W Broman <a href="mailto:broman@wisc.edu">broman@wisc.edu</a> </p> <h3>References</h3> <ul> <li><p> Bruno M. Tesson, Ritsert C. Jansen (2009) Chapter 3.7. Determining the significance threshold <em>eQTL Analysis in Mice and Rats</em> <b>1</b>, 20–25 </p> </li> <li><p> Churchill, G. A. and Doerge, R. W. (1994) Empirical threshold values for quantitative trait mapping. <em>Genetics</em> <b>138</b>, 963–971. </p> </li> <li><p> Rossini, A., Tierney, L., and Li, N. (2003), Simple parallel statistical computing. <em>R. UW Biostatistics working paper series</em> University of Washington. <b>193</b> </p> </li> <li><p> Tierney, L., Rossini, A., Li, N., and Sevcikova, H. (2004), The snow Package: Simple Network of Workstations. Version 0.2-1. </p> </li></ul> <h3>See Also</h3> <ul> <li><p> The MQM tutorial: <a href="https://rqtl.org/tutorials/MQM-tour.pdf">https://rqtl.org/tutorials/MQM-tour.pdf</a> </p> </li> <li> <p><code><a href="MQM.html">MQM</a></code> - MQM description and references </p> </li> <li> <p><code><a href="mqmscan.html">mqmscan</a></code> - Main MQM single trait analysis </p> </li> <li> <p><code><a href="mqmscanall.html">mqmscanall</a></code> - Parallellized traits analysis </p> </li> <li> <p><code><a href="mqmaugment.html">mqmaugment</a></code> - Augmentation routine for estimating missing data </p> </li> <li> <p><code><a href="mqmautocofactors.html">mqmautocofactors</a></code> - Set cofactors using marker density </p> </li> <li> <p><code><a href="mqmsetcofactors.html">mqmsetcofactors</a></code> - Set cofactors at fixed locations </p> </li> <li> <p><code><a href="mqmpermutation.html">mqmpermutation</a></code> - Estimate significance levels </p> </li> <li> <p><code><a href="scanone.html">scanone</a></code> - Single QTL scanning </p> </li></ul> <h3>Examples</h3> <pre> data(multitrait) # impute missing genotype data multitrait <- fill.geno(multitrait) ## Not run: # Calculate the thresholds result <- mqmscanfdr(multitrait, threshold=10.0, n.perm=1000) ## End(Not run) </pre> <hr /><div style="text-align: center;">[Package <em>qtl</em> version 1.66 <a href="00Index.html">Index</a>]</div> </body></html>