Email updates

Keep up to date with the latest news and content from BMC Proceedings and BioMed Central.

This article is part of the supplement: Proceedings of the 15th European workshop on QTL mapping and marker assisted selection (QTLMAS)

Open Access Open Badges Proceedings

Comparison of the analyses of the XVth QTLMAS common dataset II: QTL analysis

Olivier Demeure12*, Olivier Filangi12, Jean-Michel Elsen3 and Pascale Le Roy12

Author Affiliations

1 INRA, UMR1348 PEGASE, Domaine de la Prise, 35590 Saint-Gilles, France

2 Agrocampus OUEST, UMR1348 PEGASE, 65 rue de St Brieuc, 35042 Rennes, France

3 INRA, UR0631 SAGA, Chemin de Borde Rouge, BP 52627, 31326 Castanet-Tolosan, France

For all author emails, please log on.

BMC Proceedings 2012, 6(Suppl 2):S2  doi:10.1186/1753-6561-6-S2-S2

Published: 21 May 2012



The QTLMAS XVth dataset consisted of the pedigrees, marker genotypes and quantitative trait performances of 2,000 phenotyped animals with a half-sib family structure. The trait was regulated by 8 QTL which display additive, imprinting or epistatic effects. This paper aims at comparing the QTL mapping results obtained by six participants of the workshop.


Different regression, GBLUP, LASSO and Bayesian methods were applied for QTL detection. The results of these methods are compared based on the number of correctly mapped QTL, the number of false positives, the accuracy of the QTL location and the estimation of the QTL effect.


All the simulated QTL, except the interacting QTL on Chr5, were identified by the participants. Depending on the method, 3 to 7 out of the 8 QTL were identified. The distance to the real location and the accuracy of the QTL effect varied to a large extent depending on the methods and complexity of the simulated QTL.


While all methods were fairly efficient in detecting QTL with additive effects, it was clear that for non-additive situations, such as parent-of-origin effects or interactions, the BayesC method gave the best results by detecting 7 out of the 8 simulated QTL, with only two false positives and a good precision (less than 1 cM away on average). Indeed, if LASSO could detect QTL even in complex situations, it was associated with too many false positive results to allow for efficient GWAS. GENMIX, a method based on the phylogenies of local haplotypes, also appeared as a promising approach, which however showed a few more false positives when compared with the BayesC method.