Report on Quantitative Trait Loci (QTL) Mapping Software
A workshop was held in February, 1997, on Quantitative Genetics
and Biotechnology to survey participants on the attributes of
several software packages for mapping QTL and to define the analytical
needs which are not presently met by software packages. The workshop
covered software for mapping QTL in inbred and outbred populations.
Only the former are covered in this report.
for Crosses Between Inbred Lines
Steve J. Knapp
[ed. by Brian S. Yandell]
World Wide Web links to many, if not most, of the QTL mapping software
packages can be found at a website created by Brian S. Yandell
(Departments of Statistics and
University of Wisconsin-Madison):
Developers and users may wish to alert Brian
(firstname.lastname@example.org) to packages which are not listed
at the website. Other websites that maintain genetic software lists
include the following:
The packages discussed at the workshop were:
Two additional packages were mentioned:
QTL Cartographer (Basten, Weir & Zeng,
North Carolina State University),
PLABQTL (Utz and Melchinger, University of Stuttgart-Hohenheim),
Mapmaker/QTL (Lander et al., Whitehead
Map Manager (Keith Manley et al.),
QGene (Nelson and Tanksley, Cornell University).
A concensus was reached that there is considerable overlap in the kinds
of matings handled and statistics produced by the various QTL mapping
software packages. Some packages are more sophisticated in their
handling of genome search and multilocus QTL parameter estimation.
The software research and development needs identified by workshop
participants are detailed below. Comments specific to certain
packages or from software developers are indented. Further comments
are most welcome.
Thanks to coordinating efforts of Bill Beavis, Pioneer Seed, IA, and
comments from the following software developers:
- Multilocus Interval Mapping. Several packages use interval
mapping methods, but none have built in functions for multilocus
interval mapping (simultaneous search of more than one chromosome
MapQTL has an easy to use (and very fast) method to fit approximate multiple
QTL models according to the MQM mapping procedure of R.C. Jansen (various
papers, a.o. Jansen, 1993, Genetics 135: 205-211; Jansen, 1994, Genetics 138:
871-881). In the implemented part of MQM mapping, markers are used as
cofactors to absorb the effects of nearby QTLs, thereby increasing the
power for mapping other segregating QTLs. A future version will contain a
procedure for automatic selection of cofactor markers; currently this has
to be done by hand.
- Discrete Traits. Although methods have been described for
mapping discrete or ordinal traits, e.g., threshold traits and disease
resistance traits with arbitrary scales, none of the packages explicitly
handles such traits.
For non-normally distributed traits MapQTL contains the option to perform the
nonparametric Kruskal-Wallis rank sum test per marker.
Multi-locus models are not handled this way.
- Dominant Markers. When populations are segregating for dominant
and codominant markers, interval mapping functions are needed for
dominant x dominant, dominant x codominant, and codominant x codominant
Most interval mapping packages only handle the latter.
One package, QTL-Cartographer, handles mixtures of dominant and
MapQTL also handles all mixtures of dominant and codominant markers. MapQTL
even handles mixtures of all segregation types in crosses with outbreeders,
where per locus two up to four different alleles may segregate!
Dominant markers: Marker types (dominant are codominant) are
freely choosable in PLABQTL.
- Multivariate Analyses. Multivariate QTL mapping methods are
needed to estimate QTL genetic correlations and correlated QTL effects
and for estimating marker-assisted selection index parameters for
- QTL Cargtographer has some facility for this (Jiang and Zeng,
1996, Genetics), although it is not well documented at this time.
- Random and Mixed Effects Models. QTL or marker effects are
often handled as fixed effects in QTL mapping experiments. There are
circumstances where they might be handled as random effects, e.g., QTL
or marker variance component estimates are needed for implementing
marker-assisted index selection. There is merit to developing software
for handling QTL or marker effects as random effects. Random effects
interval mapping methods have been described, but have not yet been
implemented in any QTL mapping software packages. QTL mapping software
does not presently handle a full gamut of mixed linear model problems,
e.g., experiment and environment designs, random polygenic variances,
Experiment and environment design problems are typically handled by
estimating progeny least square means using Statistical Analysis System
(SAS) software or other software for linear models and using the progeny
means as input data for QTL mapping software.
PLABQTL computes an ad-hoc ANOVA to estimate varance components
for QTL and QTLxenvironment. But still the procedure should
[Utz] miss[es] as a point the overestimation of genetic effects and
the proportion of genetic variance explained by QTL, in other words
the influence of model selection in the QTL detection
process. Breeders should have unbiased estimates to evaluate MAS
vs. conventional selection. Or is those hidden in point 5 ?
- Marker-Assisted Selection Index Parameters. Software has not
been developed for directly estimating marker-assisted index selection
parameters (marker and genetic variances and marker index weights and
scores). Estimates of these parameters must be patched together from
the output of QTL mapping software or widely used statistical analysis
packages (e.g., SAS). Some of the technical details for estimating MAS
index parameters are not trivial and have not been worked out, e.g.,
selecting for two or more traits or across two or more generations.
The ultimate package would permit the user to handle a wide variety of
random and mixed effects estimation and selection index problems.
This looks for a daily application of MAS. Then, [Utz] thinks,
programs must be more user-friendly to allow people in the lab or
field conducting MAS and combining observation and marker data. At
the moment, programs are more for the skilled calibrator or
- Backcross Inbred Matings. Most of the packages surveyed handle
backcross, doubled haploid, recombinant inbred, and F2, F3, ..., Ft
progeny. None explicitly handle advanced backcross generations (BC2,
BC3, ..., BCt) or inbred lines developed from backcross or advanced
backcross generations (other than BC1S1).
These have been implemented in QTL Cartographer.
QGene may handle these matings as well.
MapQTL handles the following population types (one population is analyzed at
backcross (BC1), F2, any generation recombinant inbreds, (doubled) haploids
and full-sib family of a cross pollinating species (i.e. a cross between
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Last modified: Wed Jul 16 16:38:02 1997 by Brian Yandell