MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments - PubMed (original) (raw)

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

Jean-Baptiste Veyrieras et al. BMC Bioinformatics. 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.

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Figures

Figure 1

Figure 1

Comparison of model choice criteria: a simulation study. Simulation results for different values of the true number of QTL, K, and the number of observed QTL, q. The vertical bars indicate the probability that the best model selected by the criterion is the true model. The open circles, respectively the dotted lines, represent the mean, respectively the 0.1% and 0.9% quantiles, of the ratios MSEP(2)/MSEP(1) for each criterion.

Figure 2

Figure 2

Performance of AIC: a simulation study. Behavior of the AIC criterion for the 4 distance constraints, _δ_min = 1, 2, 3, 4, and for different values of the true number of QTL, K, and the number of observed QTL, q. The vertical bars indicate the probability than the AIC criterion has selected the true model. The open circles, respectively the dotted lines, represent the mean, respectively the 0.1% and 0.9% quantiles, of the ratios MSEP(2)/MSEP(1).

Figure 3

Figure 3

Overview of the maize chromosomes 8 together with the consensus chromosome. Overview of chromosome 8 for the 18 mapping experiments involved in the meta-analysis of flowering time in maize. The first chromosome at the left represents the consensus chromosome obtained by applying the WLS approach as described in the first section of the article (implemented into ConsMap). The filled marker intervals indicate that the standardized residual between the interval distance estimates of the original chromosome and the consensus one exceeded the double-sided 95% percentile of a normalized centered gaussian. This figure has been created by the program MMapView.

Figure 4

Figure 4

Visualization of the QTL meta-analysis result on maize chromosome 8. Result of the meta-analysis of the 34 QTL projected on the consensus chromosome 8. The CI of the meta-QTL positions are indicated on the chromosome by the filled colored areas. The observed QTL positions are depicted by their most probable position (triangle) and CI (segment). Membership probabilities of each initial QTL with respect to meta-QTL is visualized by the proportions of corresponding colored segments. This figure has been created by using MQTLView.

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