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Open Access Methodology article

MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments

Jean-Baptiste Veyrieras1*, Bruno Goffinet2 and Alain Charcosset1

Author Affiliations

1 UMR, INRA UPS-XI INAPG CNRS Génétique Végétale, Ferme du Moulon, 91190 Gif-sur-Yvette, France

2 BIA, Chemin de Borde Rouge BP27 31326, Castanet Tolosan Cedex, France

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BMC Bioinformatics 2007, 8:49  doi:10.1186/1471-2105-8-49

Published: 8 February 2007

Abstract

Background

Integration of multiple results from Quantitative Trait Loci (QTL) studies is a key point to understand the genetic determinism of complex traits. Up to now many efforts have been made by public database developers to facilitate the storage, compilation and visualization of multiple QTL mapping experiment results. However, studying the congruency between these results still remains a complex task. Presently, the few computational and statistical frameworks to do so are mainly based on empirical methods (e.g. consensus genetic maps are generally built by iterative projection).

Results

In this article, we present a new computational and statistical package, called MetaQTL, for carrying out whole-genome meta-analysis of QTL mapping experiments. Contrary to existing methods, MetaQTL offers a complete statistical process to establish a consensus model for both the marker and the QTL positions on the whole genome. First, MetaQTL implements a new statistical approach to merge multiple distinct genetic maps into a single consensus map which is optimal in terms of weighted least squares and can be used to investigate recombination rate heterogeneity between studies. Secondly, assuming that QTL can be projected on the consensus map, MetaQTL offers a new clustering approach based on a Gaussian mixture model to decide how many QTL underly the distribution of the observed QTL.

Conclusion

We demonstrate using simulations that the usual model choice criteria from mixture model literature perform relatively well in this context. As expected, simulations also show that this new clustering algorithm leads to a reduction in the length of the confidence interval of QTL location provided that across studies there are enough observed QTL for each underlying true QTL location. The usefulness of our approach is illustrated on published QTL detection results of flowering time in maize. Finally, MetaQTL is freely available at http://bioinformatics.org/mqtl webcite.