ArticleBMC bioinformatics2007
MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 136 papers, 40 of them syntheses that pooled it.
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Who cites it
136 citing papers in PubMed, 40 syntheses or guidelines pooled it.
- Integrated Meta-QTL analysis and transcriptomic profiling reveal genomic regions for fruit quality, abiotic and biotic stress resilience in cucumber (Cucumis sativus L.).BMC plant biology · 2026Pooled it
- Unravelling consensus genomic regions conferring stem-related traits in wheat via meta-QTL analysis.BMC plant biology · 2025Pooled it
- Whole-genome meta-analysis coupled with haplotype analysis reveal new genes and functional haplotypes conferring pre-harvest sprouting in rice.BMC plant biology · 2025Pooled it
- Priority actions for Fusarium head blight resistance in durum wheat: Insights from the wheat initiative.The plant genome · 2025Pooled it
- Meta QTL analysis for dissecting abiotic stress tolerance in chickpea.BMC genomics · 2024Pooled it
- Integrated meta-analysis and transcriptomics pinpoint genomic loci and novel candidate genes associated with submergence tolerance in rice.BMC genomics · 2024Pooled it
- Meta-analysis of identified genomic regions and candidate genes underlying salinity tolerance in rice (Oryza sativa L.).Scientific reports · 2024Pooled it
- Meta-QTL s and haplotypes for efficient zinc biofortification of rice.The plant genome · 2023Pooled it
- Meta-analysis of the quantitative trait loci associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits in pigeonpea (Cajanus cajan L.).The plant genome · 2023Pooled it
- Consensus genomic regions for grain quality traits in wheat revealed by Meta-QTL analysis and in silico transcriptome integration.The plant genome · 2023Pooled it
- Combination of meta-analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean.The plant genome · 2023Pooled it
- Meta-QTL analysis and in-silico transcriptome assessment for controlling chlorophyll traits in common wheat.The plant genome · 2023Pooled it
- Genome-wide association study to identify genomic loci associated with early vigor in bread wheat under simulated water deficit complemented with quantitative trait loci meta-analysis.G3 (Bethesda, Md.) · 2023Pooled it
- Deciphering key genomic regions controlling flag leaf size in wheat via integration of meta-QTL and in silico transcriptome assessment.BMC genomics · 2023Pooled it
- Identification of quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.).BMC plant biology · 2022Pooled it
- Genome-wide meta-QTL analyses provide novel insight into disease resistance repertoires in common bean.BMC genomics · 2022Pooled it
- Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification.Scientific reports · 2022Pooled it
- Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding.Scientific reports · 2022Pooled it
- Meta-QTLs for multiple disease resistance involving three rusts in common wheat (Triticum aestivum L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Pooled it
- Pooled it
76 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntegration 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).
resultsIn 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.
conclusionWe 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.