Paper Info
Title
MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments.
Abstract
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).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.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.
Year
DOI
Venue
2007
10.1186/1471-2105-8-49
BMC Bioinformatics
Keywords
Field
DocType
meta analysis,qtl mapping,empirical method,microarrays,gaussian mixture model,confidence interval,mixture model,bioinformatics,genetics,algorithms,quantitative trait loci
Quantitative trait locus,Visualization,Computer science,Software,Artificial intelligence,Bioinformatics,Meta-analysis,Genetics,Computer graphics,Empirical research,Machine learning,Meta-Analysis as Topic
Journal
Volume
Issue
ISSN
8
1
1471-2105
Citations 
PageRank 
References 
16
0.95
2
Authors
3
Name
Order
Citations
PageRank
Jean-Baptiste Veyrieras1344.19
Bruno Goffinet2160.95
Alain Charcosset3222.93