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This article is part of the supplement: Proceedings of the 13th European workshop on QTL mapping and marker assisted selection

Open Access Proceedings

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

Chris Maliepaard1*, John W M Bastiaansen2, Mario P L Calus3, Albart Coster2 and Marco C A M Bink4

Author affiliations

1 Plant Breeding, Wageningen University, Wageningen, The Netherlands

2 Animal Breeding and Genomics Centre, Wageningen University, Wageningen, The Netherlands

3 Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Lelystad, The Netherlands

4 Biometris, Wageningen UR, Wageningen, The Netherlands

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Citation and License

BMC Proceedings 2010, 4(Suppl 1):S2  doi:10.1186/1753-6561-4-S1-S2

Published: 31 March 2010

Abstract

Background

Five participants of the QTL-MAS 2009 workshop applied QTL analyses to the workshop common data set which contained a time-related trait: cumulative yield. Underlying the trait were 18 QTLs for three parameters of a logistic growth curve that was used for simulating the trait.

Methods

Different statistical models and methods were employed to detect QTLs and estimate position and effect sizes of QTLs. Here we compare the results with respect to the numbers of QTLs detected, estimated positions and percentage explained variance. Furthermore, limiting factors in the QTL detection are evaluated.

Results

All QTLs for the asymptote and the scaling factor of the logistic curve were detected by at least one of the participants. Only one out of six of the QTLs for the inflection point was detected. None of the QTLs were detected by all participants. Dominant, epistatic and imprinted QTLs were reported while only additive QTLs were simulated. The power to map QTLs for the inflection point increased when more time points were added.

Conclusions

For the detection of QTLs related to the asymptote and the scaling factor, there were no strong differences between the methods used here. Also, it did not matter much whether the time course data were analyzed per single time point or whether parameters of a growth curve were first estimated and then analyzed.

In contrast, the power for detection of QTLs for the inflection point was very low and the frequency of time points appeared to be a limiting factor. This can be explained by a low accuracy in estimating the inflection point from a limited time range and a limited number of time points, and by the low correlation between the simulated values for this parameter and the phenotypic data available for the individual time points.