% MultiRegress -I 30 -t 4
Does a stepwise regression with backward elimination steps for the dataset. All three traits are analyzed and both additive and dominance effects are estimated.
One can also speed up the process of finding QTL using multiple interval mapping. The core algorithms of MImapqtl are very compute intensive. As an example, using MImapqt to search for QTL de novo takes 934 seconds on a Macintosh G4 with an 867 MHz processor. Contrast this with the following sequence:
% JZmapqtl -X mletest -M 9 -A -V % MultiRegress -A -V % Rqtl -i mletest.mr -o mletestPhase0.mqt % MImapqtl -p 1 -IsMPrtseC
Converting the data with JZmapqtl and searching for putative QTL with MultiRegress yields a starting point for MImapqtl. Rqtl translates the output of MultiRegress so that MImapqtl can use it as an initial model. The -p 1 option tells MImapqtl to set the phase variable to one, and thus the program expects the input model to be in mletestPhase0.mqt. This method takes about 25 seconds and comes up with a very similar set of QTL as using MImapqtl to search from scratch.