coefficient of the biomass variable in the corresponding prediction models. This tendency is weaker in the case of Col-heterosis than in the case of C24-heterosis, as the predictive power of the pure biomass model is 0.44 for C24-heterosis and 0.35 for Col-heterosis in LOOV. The combination of the RILs’ metabolic markers and the biomass variable as predictors yields a predictive power of 0.51 and 0.47 for C24- and for Col-heterosis respectively. These numbers correspond approximately to the predictive power of the combined metabolite-genetic models (cf. Table S1). Furthermore the number of essential variables is massively reduced in the metabolite-biomass models as compared to the metabolite-genetic models (cf. Table S2). Hence, in combination with metabolite measurements, the biomass variable is more efficient than the genetic markers. On the other hand, we added the genetic markers as predictors to the pure biomass model. In that way, the predictive power has been increased in both testcross populations to an almost equal amount, from 0.44 and 0.35 up to 0.60 and 0.50 respectively, for the C24 and the Col-0 testcrosses, independently of the presence or absence of metabolic markers as predictors. In both testcross populations, these numbers represent a significant improvement, as compared to all considered models (cf. Table S1). But because of the difference of predictive power in the two testcross populations, which depends clearly on the different predictive power of the biomass variable itself, the latter is - though a strong - but not a steady predictor for heterosis according to different accessions. Dominance effects as an explanation for further differences in C24 and Col-0 models Some of the predictors, such as parental biomass, are significantly correlated to the heterosis effect in at least one of the testcross populations. Accordingly, their contribution in the respective multivariate models is high. Such variables show an interesting difference in their influence on heterosis prediction in the two testcross populations. In contrast to the biomass variable, which correlates stronger with C24-heterosis (Pearson correlation: 20.45) than with Col- heterosis (Pearson correlation: 20.36), the remaining highly predictive markers are more important for Col- than for C24- heterosis. The most important single effect is that of the first genetic marker at top of chromosome 4. Its correlation with Col- heterosis is 0.40, as compared to 0.17 with C24-heterosis. The four subsequent markers on top of chromosome 4 also have a higher single effect on Col-heterosis (correlations between 0.31 and 0.35) than any marker has on C24-heterosis, where the strongest correlation (apart from the biomass variable) is 20.20 with a specific marker in the middle of chromosome 3. Since the most important single effects are stronger on Col- heterosis than on C24-heterosis, the contribution of the corre- sponding markers in the multivariate genetic model is also higher in the case of Col-heterosis (cf. Figure S2). Here a small number of highly predictive markers is already sufficient to describe the response. In consequence, the minimal genetic prediction model for C24-heterosis contains much more variables than the corresponding Col-heterosis model (cf. Table S2). Naturally, this affects also the combined genetic-metabolic models (cf. Table S2), not only because the most important metabolic markers also have a slightly stronger effect on Col- heterosis, but above all because in the Col-0 model the incorporation of the marker at top of chromosome 4 boosts the predictive power in one step, whereas in the C24 model many less Heterosis Prediction Markers important markers are necessary for the same increase (cf. Figure 2). These observations about the substantial influence of top of chromosome 4 for Col-heterosis (VIP-ranking 1 in the minimal genetic model) in connection with its minor influence on C24- heterosis (VIP-ranking 22 in the minimal genetic model) can be explained in the following way. It is crucial for strong Col-heterosis that the respective testcross is heterozygous at top of chromosome 4, i.e. that the corresponding parental RIL carries C24 alleles at this locus, indicated by a positive coefficient of the marker in the model (cf. Methods section for the coding of the genetic data). For C24 testcrosses it does not matter (as much) which allele is present at this special genetic region. This fact suggests that C24 has a (partial) dominance effect at top of chromosome 4. We assume the dominance effect to be a partial one [26,27], since in the C24 model the presence of C24 alleles of the corresponding RIL parent has still a slightly significant positive effect on the degree of heterosis. This is confirmed by the detection of a biomass QTL with a partially dominant effect in this region (cf. Meyer et al. 2008 submitted). We found an opposite trend for the genetic marker in the middle of chromosome 3, which is only important for C24- heterosis (VIP-ranking 5 in the minimal genetic model). Indicated by the negative sign of the coefficient, the respective RIL parent is required to carry Col-0 alleles at this locus to yield high heterosis in C24 testcrosses, whereas it does not matter for Col-0 testcrosses whether they are homozygous or heterozygous at this locus. Consequently Col-0 is assumed to be dominant at the middle of chromosome 3. The same rational is applicable for some other accession-specific markers at other loci. Nevertheless, as men- tioned above, we have to take into account also epistatic effects, in consequence of which the single dominance effects are often not as clearly revealed. The metabolic balance concept indicated by metabolic markers with inverse contribution to C24- and Col- heterosis prediction Concerning the metabolic markers important for C24- and Col- heterosis prediction, there are some of them, whose model coefficients are positive in the Col-0 model while negative in the corresponding C24-model (e.g. glycine) and vice versa. This means, for instance in the case of glycine, that high levels in the parental RIL are associated with high Col-heterosis but with low heterosis in the C24 testcross. On the other hand, we find in the Col-0 parent a low level of glycine, whereas it is high in the C24 parent (and moderate in the F1 progeny). Consequently, the fact that a Col-0 testcross shows stronger heterosis when the corresponding RIL parent has a high glycine level, and a C24 testcross performs better when the corresponding RIL parent has a low glycine level, suggests that heterosis is dependent on an optimally balanced level of certain metabolites. That is in agreement with the established metabolic balance concept of heterosis, which requires the coordination of all reactions and systems for efficient growth under a given environment [28,29]. Even earlier a better metabolic balance in the hybrids [30] or metabolic control of fluxes [31] has been proposed as possible mechanisms for heterosis. This idea is also supported by some of our specific metabolic markers (cf. Table 2), whose VIP-ranking is high in one testcross setting, while low in the other one. For instance the salicylic acid marker is strongly contributing to C24-heterosis prediction (VIP- ranking on position 7), but of minor importance in our models for predicting Col-heterosis (cf. Tables S4 and S5). Interestingly, the level of salicylic acid again differs in the parental accessions C24 PLoS ONE | www.plosone.org 8 April 2009 | Volume 4 | Issue 4 | e5220