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

Veyrieras JB, Goffinet B, Charcosset A - BMC Bioinformatics (2007)

Bottom Line: However, studying the congruency between these results still remains a complex task.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: UMR, INRA UPS-XI INAPG CNRS Génétique Végétale, Ferme du Moulon, 91190 Gif-sur-Yvette, France. veyrieras@moulon.inra.fr

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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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.
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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.

Mentions: The consensus linkage group of chromosome 8 is depicted in Figure 3. The goodness-of-fit of the consensus chromosome is relatively bad: λ = 365.31 with λ ~ . It could be due to some heterogeneities in recombination rate among mapping experiments, located in the filled marker intervals of Figure 3. Note that variability of recombination rate in maize was first reported by [40] and, more recently, [41] demonstrated that exotic inbred lines exhibit higher recombination rate that U.S. inbreds origin along chromosome 1 (see also [42]). On the other hand, since no information about the marker configurations in each individual mapping experiment was available, the variances of the distance estimates have been computed by assuming no missing data and no ambiguities (dominance) in the original data sets. This is surely too optimistic and some data sets may have included missing data and/or dominant markers. Therefore the precision on the distance estimate may have been overestimated for some marker intervals.


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

Veyrieras JB, Goffinet B, Charcosset A - BMC Bioinformatics (2007)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1808479&req=5

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.
Mentions: The consensus linkage group of chromosome 8 is depicted in Figure 3. The goodness-of-fit of the consensus chromosome is relatively bad: λ = 365.31 with λ ~ . It could be due to some heterogeneities in recombination rate among mapping experiments, located in the filled marker intervals of Figure 3. Note that variability of recombination rate in maize was first reported by [40] and, more recently, [41] demonstrated that exotic inbred lines exhibit higher recombination rate that U.S. inbreds origin along chromosome 1 (see also [42]). On the other hand, since no information about the marker configurations in each individual mapping experiment was available, the variances of the distance estimates have been computed by assuming no missing data and no ambiguities (dominance) in the original data sets. This is surely too optimistic and some data sets may have included missing data and/or dominant markers. Therefore the precision on the distance estimate may have been overestimated for some marker intervals.

Bottom Line: However, studying the congruency between these results still remains a complex task.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: UMR, INRA UPS-XI INAPG CNRS Génétique Végétale, Ferme du Moulon, 91190 Gif-sur-Yvette, France. veyrieras@moulon.inra.fr

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.

Show MeSH
Related in: MedlinePlus