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Another function of Qstats is to summarize the missing data for markers, traits
and individuals. Following the histogram, there will be a table. For each trait,
it will present a summary of missing data for each marker in turn. The table
will consist of seven columns. The first three columns indicate the chromosome,
marker number and name of the marker (if there is a marker name). The fourth
column specifies what type of marker Qstats thinks it is. There are three types
that are recognized. The first is codominant and is indicated by a ``co'' token.
The other two are dominant markers and Qstats distinguishes between marker
systems in which 
is dominant to 
(indicated by the token ``A-'') and
those in which is dominant to (``a-''). Column 5 has the counts of
individuals with data for the marker, while column 6 has the counts of
individuals with both marker and trait data. Column seven is just the ratio of
columns 5 and 6.
At the end of the Qstats output file, there will be a summary of missing data for each individual in the data set. Qstats will indicate the number of marker systems, quantitative traits and categorical traits. It will then have a table with seven columns. Column 1 is for the individual. Column 2 indicates the number of markers for which the individual is typed, and Column 3 indicates a percent. Columns 4 and 5 do the same for traits while columns 6 and 7 summarize the data for categorical traits.
Something to keep in mind is that some of the analyses require large sample sizes. For example, if the sample sizes are too small, the ECM algorthm may fail in Zmapqtl. When difficulties in analysis are encountered, check the missing data summaries in the Qstats output: Such problems often correspond to areas with a lot of missing data.