JZmapqtl - Multitrait mapping module
JZmapqtl [ -o output ] [ -i input ] [ -m mapfile ] [ -E eqtfile ] [ -S srfile ] [ -t trait ] [ -M Model ] [ -c chrom ] [ -d walk ] [ -n nbp ] [ -w window ] [ -I hypo ]
JZmapqtl uses (composite) interval mapping to map quantitative trait loci to a map of molecular markers and can analyze multiple traits simultaneously. It requires a molecular map that could be a random one produced by Rmap, or a real one in the same format as the output of Rmap. The sample could be a randomly generated one from Rcross or a real one in the same format as the output of Rcross. In addition, the program requires the results of the stepwise linear regression analysis of SRmapqtl for composite interval mapping.
See QTLcart(1) for more information on the global options -h for help, -A for automatic, -V for non-Verbose -W path for a working directory, -R file to specify a resource file, -e to specify the log file, -s to specify a seed for the random number generator and -X stem to specify a filename stem. The options below are specific to this program.
If you use this program without specifying any options, then you will get into a menu that allows you to set them interactively.
The input format of the molecular map should be the same as that of the output format from the program Rmap. The input format of the individual data should be the same as the output format of the program Rcross.
% JZmapqtl
Calculates the likelihood ratio test statistics of the dataset in qtlcart.cro using the map in qtlcart.map.
% nice JZmapqtl -A -V -i corn.cro -m corn.map -M 6 -t 3 -I 34 &
Calculates the likelihood ratio test statistics of the dataset in corn.cro using the map in corn.map. Model 6 is used for analysis. This file has two traits, so specifying trait 3 means that both traits are analyzed. Hypothesis 34 means that GxE interactions are also analyzed. The program is nice'd as a courtesy to other users, and run in the background so that the user can logout and relax.
Different parameters for the -M option allow for the analysis of the data assuming different models. See the Zmapqtl man page for explanations of models 3 and 6. These are the only models available in JZmapqtl
Preplot ignores the output at present. So far, the program only does joint mapping and one form of GxE. Tests for close linkage, pleiotopic effects and other environmental effects will be added in the future.
You can select traits to include in the analysis in three ways:
You need to set the hypothesis test for SFx and RFx crosses. The default of 10 is ok for crosses in which there are only two marker genotypic classes (BCx, RIx). To test GxE, use 14. For SFx and RFx, values of 30, 31 or 32 are valid, and a 34 invokes the GxE test. Recall that we have the following hypotheses:
For 30, we test H3:H0. For 31, we test H3:H0, H3:H1 and H1:H0. For 32, we test H3:H0, H3:H2 and H2:H0. 30 is probably fine for initial scans. Hypothesis 34 does a test for H3:H0 as well as the GxE.
For Model 6, be sure to run SRmapqtl first. Once done, JZmapqtl will use all markers that are significant for any of the traits in the analysis. We need to work out a better way to select the cofactors. Presently we use any markers that are significant for any trait. Also, be sure to use FB regression (Model 2 in SRmapqtl), or else you will end up using all markers as cofactors.
Rmap(1), Rqtl(1), Rcross(1), Qstats(1), LRmapqtl(1), SRmapqtl(1), Zmapqtl(1), Eqtl(1), Prune(1), Preplot(1), QTLcart(1)
In general, it is best to contact us via email (basten@statgen.ncsu.edu)
Christopher J. Basten, B. S. Weir and Z.-B. Zeng
Department of Statistics, North Carolina State University
Raleigh, NC 27695-8203, USA
Phone: (919)515-1934