Bioinformatics Advance Access originally published online on August 19, 2004
Bioinformatics 2005 21(1):128-130; doi:10.1093/bioinformatics/bth481
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Bioinformatics vol. 21 issue 1 © Oxford University Press 2005; all rights reserved.
MCQTL: multi-allelic QTL mapping in multi-cross design
INRA, Unité de Biométrie et d’Intelligence Artificielle B.P. 27, 31326 Castanet-Tolosan Cedex, France
*To whom correspondence should be addressed.
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODSIMPLEMENTATIONREFERENCES |
|---|
Summary: The aim of the MCQTL software package is to perform QTL mapping in multi-cross designs. It allows the analysis of the usual populations derived from inbred lines and can link the families by assuming that the QTL locations are the same in all of them. Moreover, a diallel modelling of the QTL genotypic effects is allowed in multiple related families.
The implemented model is a linear regression model. A composite interval mapping and an iterative QTL mapping are implemented to deal with multiple QTL models. Marker cofactor selections by forward or backward stepwise methods are implemented as well as computation of threshold test value by permutation.
Availability: The program is available on request after signing a licence agreement; free of charge for academic and non-profit organizations at http://www.genoplante.org(Bioinformatics products).
Contact: contactbioinf{at}genoplante.com
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSIMPLEMENTATIONREFERENCES |
|---|
Many QTL mapping computer programs are available to analyse data collected in experimental crosses. Most of them are designed to analyse line crosses, one family at once. They were reviewed in Manly and Olson, 1999, except for the most recent one, R/qtl Broman et al., 2003 and are listed in the web site http://linkage.rockfeller.edu/soft.
Combining different line crosses for mapping QTL has been shown by simulation studies to be powerful, whatever the choice of the QTL model: random effect, fixed intra-family effect or diallel modelling Muranty, 1996, Xie et al., 1998, Xu, 1998, Rebaï and Goffinet, 2000. Moreover, this strategy was successfully applied with mouse inbred lines Hitzemann et al., 2002 and is current in dairy cattle Khatkar et al., 2004. However, few mapping computer programs are available to perform QTL analysis in multiple families. QTL express Seaton et al., 2002 permits analysis of complex pedigree as the half-sib design. It fits a general linear regression model and is used via a web server. INTERQRL Jannink and Wu, 2003 and FlexQTLTMBink et al., 2004 perform QTL mapping in a Bayesian framework.
MCQTL is a local UNIX application. It is designed to perform QTL mapping using linear regression model in multiple families of line crosses with fixed intra-family QTL effects or diallel modelling. An original method to automatically find a multiple QTL model is implemented. Obviously, the analysis of a single cross is also feasible.
|
METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSIMPLEMENTATIONREFERENCES |
|---|
MCQTL package is comprised of three software applications. The first component, TranslateData reads data from MAPMAKER Lincoln et al., 1993 like files. The second component, ProbaPop computes QTL genotype probabilities given marker information at each chromosome location for each family and stores them in XML formatted files. The last component, Multipop builds the pooled model using the genotype probabilities, computes Fisher test and estimates the model parameters. This three-step procedure is similar to the two-step procedure of Seaton et al., 2002 and therefore has the same flexibility and robustness. Indeed, users can add or drop a family and modify the model without re-computing the QTL genotype probabilities.
The intra-family model is a usual regression model Haley and Knott, 1992 with genetic cofactors and a single QTL. Let c denote the cross between two parent lines i,j, the phenotypic value Y ck of the k-th individual is modelled by
 |
where µ c is the global mean in the cross c, L–1 is the number of genetic cofactors, 
is the probability of the k-th individual having genotype ij at the QTL or cofactor locus l given the marker information, 
is the mean of the ij genotype at locus l in cross c and 
ck the residual error.
Two models are implemented to link multiple families. The first one assumes the same locations of cofactors and QTL for all the crosses but intra-family parameters. For the second one, are assumed to be independent of the cross. This implies that additive allelic effects depend only on the parent lines. In both models, intra-family residual variances are assumed equal. In this current version, only an additive model is implemented.
A resampling method by permutation of the trait data is implemented to set chromosome or genome-wide significance thresholds of a single QTL model Churchill and Doerge, 1994. The adaptation to multiple cross design is obtained by limiting permutations of the trait data to intra-family permutations. Two scan methods are implemented (i) a composite interval mapping Zeng, 1993 that consists in dropping out the cofactors belonging to a chromosome while it is scaned and (ii) an iterative QTL mapping that aims to automatically find a multiple QTL model Charcosset et al., 2001. A marker cofactor selection usually precedes the scan step. So, a forward stepwise method on the whole genome and a backward stepwise method chromosome by chromosome are implemented. Output files are XML formatted files and graphic files obtained from the open source software Gnuplot. XML format was chosen to permit easy interactions among MCQTL, CarthaGène [a genetic/radiated hybrid mapping software for multiple populations Schiex and Gaspin, 1997] and BioMercator [a software for integrating genetic maps and QTL detected in independent experiments Arcade et al., 2004]. A graphical user interface is ongoing.
Figure 1 presents the MCQTL architecture, the input and output files for the analysis of a half-diallel design from three parent lines.
|
|
IMPLEMENTATION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSIMPLEMENTATIONREFERENCES |
|---|
The TranslateData application was developed in Java and runs using a 1.4 or later JVM. ProbaPop and MultiPop, being computationally intensive, are coded in C++. A Sun-OS and a Linux archive respectively tested on Sun OS 5.9 sparc Ultra-Enterprise and Linux 2.4.4 intel computers are available. Gnuplot 3.7 or latter version (available from http://www.gnuplot.info) is required.
|
Acknowledgments |
|---|
This work was supported by GENOPLANTE project ‘Integrative Tools for Genetic Mapping’.
Received on April 29, 2004; revised on June 28, 2004; accepted on July 27, 2004
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSIMPLEMENTATIONREFERENCES |
|---|
Arcade, A., Labourdette, A., Falque, M., Mangin, B., Chardon, F., Charcosset, A., Joets, J. (2004) BioMercator: integrating genetic maps and QTL towards discovery of candidate genes. Bioinformatics, 20, 2324–2326
Bink, M.C.A.M., Boer, M.P., ter Braake, C.J.F., Jansen, H. (2004) A flexible Bayesian framework for multiple QTL mapping in pedigreed populations. Proceedings of PAG XII, January 10–14, , CA San Diego.
Broman, W.B., Wu, H., Sen, S., Churchill, G.A. (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics, 19, , pp. 889–890
Charcosset, A., Mangin, B., Moreau, L., Combes, L., Jourjon, M.-F., Gallais, A. (2001) Heterosis in maize investigated using connected RIL populations. Quantitative genetics and breeding methods: the way ahead. Les colloques no. 96, , Paris INRA editions.
Churchill, G.A. and Doerge, R.W. (1994) Empirical threshold values for quantitative trait mapping. Genetics, 138, , pp. 963–971[Abstract].
Haley, C.S. and Knott, S.A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity, 69, 315–324[Web of Science][Medline].
Hitzemann, R., Malmanger, B., Cooper, S., Coulombe, S., Reed, C., Demarest, K., Koyner, J., Cipp, L., Flint, J., Talbot, C., et al. (2002) Multiple cross mapping (MCM) markedly improves the localization of a QTL for ethanol-induced activation. Genes Brain Behav., 4, 214–222.
Jannink, J.-L. and Wu, X.-L. (2003) Estimating allelic number and indentity in state of QTL in interconnected families. Genet. Res., 81, 133–144[CrossRef][Web of Science][Medline].
Khatkar, M.S., Thomson, P.C., Tammen, I., Raadsma, H.W. (2004) Quantitative trait loci mapping in dairy cattle: review and meta-analysis. Genet. Sel. Evol., 36, 163–190[CrossRef][Web of Science][Medline].
Lincoln, S.E., Daly, M.J., Lander, E.S. (1993) Constructing genetic linkage maps with MAPMAKER/EXP version 3.0. A tutorial and reference manual. 3rd edn Technical Report Whitehead Institute for Biomedical Research.
Manly, K.F. and Olson, J.M. (1999) Overview of QTL mapping software and introduction to Map Manager QT. Mamm. Genome, 10, 327–334[CrossRef][Web of Science][Medline].
Muranty, H. (1996) Power of tests for quantitative trait loci detection using full-sib families in different schemes. Heredity, 76, 156–165.
Rebaï, A. and Goffinet, B. (2000) More about QTL mapping with diallel design. Genet. Res., 15, 243–247[CrossRef].
Seaton, G., Haley, C.S., Knott, S.A., Kearsey, M., Visscher, P.M. (2002) QTL Express: mapping quantitative trait loci simple and complex pedigrees. Bioinformatics, 18, 339–340
Schiex, T. and Gaspin, C. (1997) Cartagene: constructing and joining maximum likelihood genetic maps. Proceedings of ISMB'97, June , Greece Halkidiki.
Xie, C., Gessler, D.G., Xu, S. (1998) Combining different line crosses for mapping quantitative trait loci using the identical by descent-based variance component method. Genetics, 149, , pp. 1139–1146
Xu, S. (1998) Mapping quantitative trait loci using multiple families of line crosses. Genetics, 148, 517–524
Zeng, Z.-B. (1993) Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc. Natl Acad. Sci. USA, 136, 1457–1468.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  J. Burstin, P. Marget, M. Huart, A. Moessner, B. Mangin, C. Duchene, B. Desprez, N. Munier-Jolain, and G. Duc Developmental Genes Have Pleiotropic Effects on Plant Morphology and Source Capacity, Eventually Impacting on Seed Protein Content and Productivity in Pea Plant Physiology, June 1, 2007; 144(2): 768 - 781. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J.-M. Ribaut and M. Ragot Marker-assisted selection to improve drought adaptation in maize: the backcross approach, perspectives, limitations, and alternatives J. Exp. Bot., January 1, 2007; 58(2): 351 - 360. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||