Abstract
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. 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. 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. 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.
Highlights
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
Figure 1), but only cumulative yield at five time points was available to the participants
The limitations in estimating these parameters from the cumulative yield at a number of time points are different for the three parameters of the growth curve: for the asymptote (1) and the scaling factor (3), the limiting factor is mostly the heritability: correlations between the estimated parameters and the phenotypic values are very high (> 0.98 for 1, > 0.90 for 3), but the correlation between phenotypic values and genotypic values is only about 0.7
Summary
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. A third alternative approach is to employ a single-step approach in which the covariance structure between the time points is allowed for and the effects are estimated simultaneously with additional fixed and random terms such as polygenic effects and environmental factors. All these approaches have been applied successfully in QTL mapping of time-dependent traits [1,2,3,4,5,6,7,8]. The aim of this study was to evaluate methods used by participants of the QTL- MAS 2009 workshop to detect QTLs related to time course data of a common data set
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