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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

Background - Genomic selection, the use of markers across the whole genome, receives increasing amounts of attention and is having more and more impact on breeding programs. Development of statistical and computational methods to estimate breeding values based on markers is a very active area of research. A simulated dataset was analyzed by participants of the QTLMAS XIII workshop, allowing a comparison of the ability of different methods to estimate genomic breeding values. Methods - A best case scenario was analyzed by the organizers where QTL genotypes were known. Participants submitted estimated breeding values for 1000 unphenotyped individuals together with a description of the applied method(s). The submitted breeding values were evaluated for correlation with the simulated values (accuracy), rank correlation of the best 10% of individuals and error in predictions. Bias was tested by regression of simulated on estimated breeding values. Results - The accuracy obtained from the best case scenario was 0.94. Six research groups submitted 19 sets of estimated breeding values. Methods that assumed the same variance for markers showed accuracies, measured as correlations between estimated and simulated values, ranging from 0.75 to 0.89 and rank correlations between 0.58 and 0.70. Methods that allowed different marker variances showed accuracies ranging from 0.86 to 0.94 and rank correlations between 0.69 and 0.82. Methods assuming equal marker variances were generally more biased and showed larger prediction errors. Conclusions - The best performing methods achieved very high accuracies, close to accuracies achieved in a best case scenario where QTL genotypes were known without error. Methods that allowed different marker variances generally outperformed methods that assumed equal marker variances. Genomic selection methods performed well compared to traditional, pedigree only, methods; all methods showed higher accuracies than those obtained for breeding values estimated solely on pedigree relationships

During seed maturation and germination, major changes in physiological status, gene expression, and metabolic events take place. Using chlorophyll sorting, osmopriming, and different drying regimes, Brassica oleracea seed lots of different maturity, stress tolerance, and germination behavior were created. Through careful physiological analysis of these seed lots combined with gene expression analysis using a dedicated cDNA microarray, gene expression could be correlated to physiological processes that occurred within the seeds. In addition, gene expression was studied during early stages of seed germination, prior to radicle emergence, since very little detailed information of gene expression during this process is available. During seed maturation expression of many known seed maturation genes, such as late-embryogenesis abundant or storage-compound genes, was high. Notably, a small but distinct subgroup of the maturation genes was found to correlate to seed stress tolerance in osmoprimed and dried seeds. Expression of these genes rapidly declined during priming and/or germination in water. The majority of the genes on the microarray were up-regulated during osmopriming and during germination on water, confirming the hypothesis that during osmopriming, germination-related processes are initiated. Finally, a large group of genes was up-regulated during germination on water, but not during osmopriming. These represent genes that are specific to germination in water. Germination-related gene expression was found to be partially reversible by physiological treatments such as slow drying of osmoprimed seeds. This correlated to the ability of seeds to withstand stress Wetenschappelijke publicatie over de analyse van expressie van honderden genen tijdens zaadrijping, zaadpriming en zaadkieming. De expressie van de genene kan als merker gebruikt worden bij onderzoek aan vigour van zaden.