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This article is part of the supplement: Proceedings of the 14th European workshop on QTL mapping and marker assisted selection (QTL-MAS)

Open Access Proceedings

Comparison of analyses of the QTLMAS XIV common dataset. II: QTL analysis

Sebastian Mucha1*, Marcin Pszczoła123, Tomasz Strabel1, Anna Wolc14, Paulina Paczyńska1 and Maciej Szydlowski1

Author affiliations

1 Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Wolynska 33, 60-637 Poznan, Poland

2 Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands

3 Animal Breeding and Genomics Centre, Wageningen Institute of Animal Sciences Wageningen University, 6700 AH Wageningen, The Netherlands

4 Department of Animal Science and Center for Integrated Animal Genomics, Iowa State University, Ames, IA 50011-3150, USA

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Citation and License

BMC Proceedings 2011, 5(Suppl 3):S2  doi:10.1186/1753-6561-5-S3-S2

Published: 27 May 2011

Abstract

Background

A 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.

Methods

The 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.

Results

Seven 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.

Conclusions

Our 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.