For the specified trait, **SRmapqtl** will output a small table:

------------------------------------------------------- ------------------------------------------------------- Chromosome Marker Rank F-Stat DOF ------------------------------------------------------- -start 1 1 2 13.38778 114 2 3 4 10.12742 110 3 1 5 3.55528 108 3 2 3 11.15490 112 4 3 1 28.85236 116 -end ------------------------------------------------------- -------------------------------------------------------

The first two columns indicate the chromosome and marker. The third column gives the
rank of that marker as determined by the stepwise regression mode of choice.
Then there will be an F-statistic indicating the difference between having that
variable in the model or not. Finally, the DOF (degrees of freedom)
for the numerator of that F statistic
is given. For forward stepwise or backward elimination, **SRmapqtl** will try to rank
all of the markers no matter how small the F statistic is. For the forward regression
with backward elimination, the program proceeds to add variables until
the F statistic p-value is less than that specified by the *-F* option (0.1 by default).
Then **SRmapqtl** rechecks all the variables added and will eliminate any with an
F statistic p-value less than the value given with the *-B* option.

In general, the FB method is probably the best method for picking background markers
to be used with model 6 in **Zmapqtl** and **JZmapqtl**. To this end, **SRmapqtl** should be
run prior to using either module. **Zmapqtl** and **JZmapqtl**
will read the results of **SRmapqtl** and use
the markers that are ranked. You can specify an upper bound to the number of background
parameters to be used in **Zmapqtl**. **JZmapqtl** will use all the markers that are listed for
all traits in its analysis: The FB method thus selects only a subset of significant markers.

Be aware that **SRmapqtl** tries to determine how many markers can be analyzed at once. The number of
parameters has to be smaller than the sample size. If you try to use backward regression, and there
are more markers than individuals, then **SRmapqtl** will default to forward stepwise regression
and rank as many markers as possible. You should be aware that when dominance can be estimated,
each marker will count two towards the total number of parameters and you will need a sample
size of at least twice the number of markers to do backward elimination.