ArticleBMC proceedings2011
Comparison of analyses of the QTLMAS XIV common dataset. II: QTL analysis.
Article in BMC proceedings, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions.Journal of animal science and biotechnology · 2016Article
- Evaluation of the lasso and the elastic net in genome-wide association studies.Frontiers in genetics · 2013Article
- Genome-wide association study of insect bite hypersensitivity in two horse populations in the Netherlands.Genetics, selection, evolution : GSE · 2012Article
- Comparison of the analyses of the XVth QTLMAS common dataset II: QTL analysis.BMC proceedings · 2012Article
- Genomic breeding value prediction and QTL mapping of QTLMAS2011 data using Bayesian and GBLUP methods.BMC proceedings · 2012Article
- Estimating genomic breeding values and detecting QTL using univariate and bivariate models.BMC proceedings · 2011Article
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Authors and funding
6 authors.
Funding
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Abstract
backgroundA quantitative and a binary trait for the 14th QTLMAS 2010 workshop were simulated under a model which combined additive inheritance, epistasis and imprinting. This paper aimed to compare results submitted by the participants of the workshop.
methodsThe results were compared according to three criteria: the success rate (ratio of mapped QTL to the total number of simulated QTL), and the error rate (ratio of false positives to the number of reported positions), and mean distance between a true mapped QTL and the nearest submitted position.
resultsSeven groups submitted results for the quantitative trait and five for the binary trait. Among the 37 simulated QTL 17 remained undetected. Success rate ranged from 0.05 to 0.43, error rate was between 0.00 and 0.92, and the mean distance ranged from 0.26 to 0.77 Mb.
conclusionsOur comparison shows that differences among methods used by the participants increases with the complexity of genetic architecture. It was particularly visible for the quantitative trait which was determined partly by non-additive QTL. Furthermore, an imprinted QTL with a large effect may remain undetected if the applied model tests only for Mendelian genes.
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