## 2 Results and Discussion
### Examples
#### 2.2.1 Plant samples  
Identification of relationships between naturally occurring genetic and phenotypic variation in _Pinus taeda L_. Loblolly pine, a gymnosperm, may help to understand the evolution of adaptive traits in land plants [34]. In order to assign biological roles to genes of unknown function and to define gene networks of adaptive significance, a population genomic approach (association genetics) is used to study how allelic sequence variation among individuals results in phenotypic differences. Specifically, association at 1,000 loci allelic (SNP) variation with phenotypic variation is planned to investigate with the utilization of metabolic profiling technologies. Phenotypes will be measured in large populations of clonally propagated trees that represent the widest possible spectrum of genetic variation in the species. In the present study a pilot project is used as a simplified model allowing to approach/test and/or to develop sufficient methodologies for large-scale study. Six different clones of 1-year-old _Pinus taeda L_. seedlings grown under standardized conditions in a green house were used for sample preparation and further analysis [34]. In HILIC-LC/ESI-MS, RP-LC/ESI-MS, and GC/TOF-MS data, PCA plots showed significant clustering and differentiation among groups (Figures 3a, 3b). **Figure 3a. ** Upper panel: PCA-DA score and loading plots of the _Pinus taeda_ L. GC-TOF-MS data. Groups (clones) are color-coded in accordance with embedded legend. Software is MarkerView 1.0. Loading plot illustrate resolved annotated by BinBase [35, 36]. Metabolites with chemical names assigned. Unidentified ones named by the numbers. Lower panel: variation of 4-\(O\)-galactopyranosyl-d-mannopyranose levels in different six clones. **Figure 3b. ** PCA-DA score plots of the _Pinus taeda_ L. RP (left panel) and HILIC (right panel) LC-ESI-MS (B) datasets generated with MarkerView 1.0. Groups are color-coded in accordance with embedded legend. **Figure 3c. ** PCA-DA score and loading plots generated with top 10 predictors derived by GA analysis. When the cluster number was arbitrarily set as 6, kmeans and hclust functions showed similar results that group 1 and group 2 were mostly separated from the others. Without a priori knowledge of the cluster number, model-based mclust showed similar cluster numbers, while pvclust showed refinedclustering structures. It seems that pvclust and mclust are good choices for clustering if grouping information is not available. Since GA is stochastic and may give different solutions in each run or generation or model, the reproducibility of feature component selection across independent runs or models was tested. Frequencies, occurrences of feature components across different models generated from GA, were examined, and the results showed that a small set of components was frequently selected respectively (Tables 1-3). It was found that 10 most frequent predictors in HILIC-LC/ESI-MS, RP-LC/ESI-MS, and GC/TOF-MS data occurred at least 100 times in 2000 models [34]. This observation suggests the potential importance of these components as discriminative for different genotypes. Another population of 2000 models generated by the second and third identical independent runs on the same GC/TOF-MS dataset showed that the top 10 component lists were very similar, although rank sequences were slightly different (Table 1). Raffinose, tocopherol, citric acid, and compound 200401 always occurred in more than 1000 models out of the total 2000 models, suggesting that the top 10 component list would be more stabilized if enough GA solutions are allowed. For each model generated by GA, the GA procedure tests its prediction accuracy on the group membership of each sample in the dataset (fitness) and assigns a score. For HILIC, RP-LC/ESI-MS, and GC/TOF-MS data, a GA fitness solution was found within generation 200 in an average of all the models.