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.
This requires a filename for output. JZmapqtl will append the file
if it exists, and create a new file if it does not. If not used, then
JZmapqtl will use qtlcart.zj, where the j indicates the trait
analyzed and the zero'th file contains joint mapping.
Use this to specify which trait JZmapqtl will analyze. If this
number is greater than the number of traits, then all traits will be
analyzed unless the trait name begins with a minus sign. If a negative
number is given, then only traits beginning with a plus sign will be
analyzed. The default is to analyze trait 1 only.
Use this to limit the number of background parameters that JZmapqtl uses in
composite interval mapping. This is used only with model 6. It tells JZmapqtl
to use markers with rank no higher than specified with this option. Markers are
ranked by SRmapqtl.out and only those markers for traits in the analysis with
sufficient rank are used.
JZmapqtl blocks out a region of this many centiMorgans on either side
of the markers flanking the test position when picking background
markers. It is 10 by default and is only used in models 5 and 6. We
refer to it as the window size.
JZmapqtl requires the user to specify which hypotheses to test. For
backcrosses, there are two hypotheses numbered 1 and 0. Use 10 for
backcrosses or a 14 to do GxE tests as well. For crosses in which
there are three genotypic classes, there are hypotheses 0, 1, 2, and 3.
Use 30, 31, 32 in that case or 34 to do GxE. These are explained in
greater detail in the manual.
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
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 main analysis models available in
JZmapqtl. You can also use model 9, which prepares an input file for
use in MultiRegress. Mainly, it calculates the expected genotypes at the sites where it
would have done analyses. The expected genotypes are calculated according to
table 3.7 from the QTL Cartographer manual.
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.
Set the trait to analyze at 0, so that no traits except those
beginning with a [+] (plus sign) are analyzed. You would need to edit the
.cro file first to prepend a + to all traits you wanted in the
Set the trait to a value greater than t. Then all traits will be put in
the analysis, unless they begin with a minus sign [-]. As in a. above, you
would need to edit the .cro file to minus out some traits.
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
H0: a = d = 0
H1: a !=0 , d = 0
H2: a = 0 , d != 0
H3: a != 0, d != 0
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.
Christopher J. Basten, B. S. Weir and Z.-B. Zeng
Bioinformatics Research Center, North Carolina State University
1523 Partners II Building/840 Main Campus Drive
Raleigh, NC 27695-7566 USA