MapQTL ® List of frequently asked questions
Last modified: 31 January 2017.
1. How do I install my personal license file 'MAPQTL.LIC' ?
2. Why do I get the message 'MapQTL is using an evaluation license' ?
3. On which versions of MS-Windows runs MapQTL ?
4. I have lost sight of the navigation panel; how do I get it back?
5. Why do I see just two linkage groups and two numerical traits, whereas there are many more in the data files ?
6. Why do I get these irregular lod profiles with very local high lod scores?
7. What does a 'singularity error' mean?
8. What does a 'perfect fit' warning mean?
1. How do I install my personal license file 'MAPQTL.LIC' ?
MapQTL reads its license file 'MAPQTL.LIC' in its program file directory, which is typically 'C:\Program Files\MapQTLX' (X=version number 4, 5 or 6). After installation of the MapQTL software the installed copy of the license file is an evaluation license. Replace that copy with your personal copy, make sure that it is called 'MAPQTL.LIC', and the MapQTL software will become fully functional. Under Windows 7 and Vista this can only be done if you are logged on as a user with 'Administrator' privileges. The easiest way is to start MapQTL from within the installation procedure (by placing the appropriate checkmark in the final screen of the installation), and next use the 'Install License' option of the MapQTL Help-menu.
to top of page2. Why do I get the message 'MapQTL is using an evaluation license' ?
When you get this message it means that MapQTL is using a license file 'MAPQTL.LIC' that only allows an evaluation of the software, with limited functionality. When you obtained a personal license file (on your installation CDROM or by e-mail), you must replace this evaluation license file with your personal license file. This personal license file usually has a file name with the extension '.MQ6win' (or '.MQ5win'). In the replacement of the evaluation license it should become called 'MAPQTL.LIC' (see 1.).
3. On which versions of MS-Windows runs MapQTL ?
MapQTL 6 run on the 32-bit Windows platforms 7, 8, 8.1 and 10.
It will run as 32-bit software under the 64-bit versions of MS-Windows ® 7, 8, 8.1 and 10.
MapQTL 4.0 and 5 do not run properly under Vista and higher, but will run there fine under Windows XP compatibility mode.
This can be achieved by right-clicking on the .exe-file, choosing Properties, checking on the Compatibility tab
'Run this program in compatibility mode for:' and select 'Windows XP' (any service pack).
Alternatively, the
program can be run under Windows XP Mode of the Windows Virtual PC. Windows XP Mode is freely available from Microsoft
for Windows 7 Professional.
4. I have lost sight of the navigation panel; how do I get it back?
Move the mouse pointer to the left edge of the main window where the navigation panel should be.
At some point the mouse pointer will change into the dragging shape; if that happens, drag the panel
back into sight.
If you have difficulties with this approach, there is an alternative.
You should close the program, remove the file 'MapQTL.ProgSetup' in the 'My Documents\MapQTLX' directory (X=version number 5 or 6),
and start the program again; it will restore all view settings of the program.
5. Why do I see just two linkage groups and two numerical traits, whereas there are many more in the data files ?
This means that MapQTL is using a license file 'MAPQTL.LIC' that only allows an evaluation of the software, with limited functionality. When you obtained a personal license file (on your installation CDROM, diskettes or by e-mail), you must replace this evaluation license file with your personal license file, which usually has a file name with the extension '.MQ6win' (or '.MQ5win'), but may also be called 'MAPQTL.LIC' (see 1.).
6. Why do I get these irregular lod profiles with very local high lod scores?
Under certain circumstances you can get irregular lod profiles. They can be characterised by:
- very irregular lod profiles, i.e. a (very) high lod at one marker next to a low lod at a neighbouring marker at short distance,
- high lod scores often inbetween neighbouring markers that have a large distance to each other, while low lods at these markers,
- high numbers of iterations at the high lod scores (>20),
- non-normally distributed trait data (e.g. ordinal, binomial or uniform data),
- unexpected QTL genotype contrasts (e.g. at an <aaxab> type marker the major contrast is between mu_ac and mu_ad on the one hand (i.e. approximately mu_ac=mu_ad) and mu_bc and mu_bd on the other,
- lower amounts of information in the markers, e.g. dominant genotypes or <aaxab> markers and alike.
Basically, the results are a problem of the mapping algorithm, which is a maximum likelihood method for the separation of a mixture of distributions. In this algorithm the separation is based upon both genetic information, i.e. the marker genotypes, and phenotypic information, i.e. the quantitative trait data. Now, when there is not much genetic information and the trait is rather non-normal, then it may happen that the phenotypic information will outweigh the genetic information. For instance, when at an <aaxab> type marker more or less two underlying distributions could be distinguished (especially at ordinal data on a scale of 1 to 5 or so, and also at uniformly distributed data), then these distributions become fitted to the ac and bc qtl-genotypes as well as to the ad and bd qtl-genotypes (i.e. a genetic effect within the first parent in the cross P1 x P2), while there is no (direct) genetic information from the marker about segregation within the first parent. As such the fitting happens by chance to the genotypes. Therefore, a major symptom of phenotypic information outweighing the genotypic information is the large number of iterations.
Two other possible causes of irregular lod profiles are outliers in the quantitative trait data and erroneous marker data. Sometimes it can be better to replace suspect (marker or trait) data by unknowns.
To a certain extent there are some possibilities to inspect such odd results. An interval mapping analysis without map (enter 'none' as map file) or a Kruskal-Wallis analysis will show no significance on these markers. Another option is to set the 'maximum number of neighbouring markers used' parameter to a higher number; this may increase the amount of genetic information; on the other hand this may also increase the odd results or introduce them in other areas.
QTL mapping with the approach of regression on expected value (Haley & Knott, Heredity, 1992, 69: 315-324; Martinez & Curnow, Theor Appl Genet, 1992, 85: 480-488) is not affected like the maximum likelihood interval mapping approach.
7. What does a 'singularity error' mean?
This means that the expected trait values for the QTL genotypes cannot be estimated. It occurs when the probability for one (or more) of the QTL genotypes, given the marker genotypes and the position on the map for which the LOD is being calculated, is zero. Such a probability usually is never zero inbetween two markers (because there is always a non-zero probability of recombination), but it can be zero on top of a marker. This can only occur if all genotypes are known for the marker. In practice this means that in such a case the GIC value is 1.0; it can be lower but then the individuals with some missing marker information also have missing trait observations.
8. What does a 'perfect fit' warning mean?
This can only occur if the number of distinct trait observations is equal to or smaller than the number of possible QTL genotypes. Usually this happens when the analysed trait was observed on an ordinal (or even binomial) scale. In such instances it may happen that the interval mapping maximum likelihood (ML) algorithm iterates towards a situation that the different QTL genotypes coincide exactly with the different trait classes. Usually this doesn't occur on top of markers, in such a case even the marker (!) genotypes would coincide exactly with the trait observations. But it occurs most often inside intervals (i.e. in between markers) where the ML algorithm will iterate towards this situation, which is signalled as a 'perfect fit'. [NB: One of the main assumptions for interval mapping is that the trait residuals behave according to a normal distribution; this assumption is violated when interval mapping is applied on ordinal traits.]