Epidemiological research investigates the natural occurring variation of complex traits and the covariance between these traits in the general population. By doing so, epidemiological research is an important tool to understand influential factors on complex traits such as neuropsychiatric diseases and related phenotypes. However, epidemiological studies are challenged by interpretational difficulties and are often limited to inferential data analysis especially when based on a cross-sectional design. Different strategies exist to optimize the impact generated by such inferential data analyses. One strategy is to increase the depth of information by adding intermediate related traits, which is especially done in the field of genetics. However, complex covariance pattern typically underlie the relation between e.g. genotype, intermediate phenotype and primary phenotype of interest, which have to be resolved. In this situation, more complex analytical strategies might help to identify the most plausible model of relationship. The downside of these more comprehensive analytical models lies in the increase of model complexity, that might result in a less stable outcome. Finding a good balance between model complexity and analytical simplicity is a major challenge when performing combined analyses with several complex phenotypes. In the current thesis I presented three different works dealing with complex analytical strategies. The main goal behind all three of them was not to build up comprehensive theoretical frameworks, but to perform more preparatory analytical steps. The meta-analysis validated and extended a genetic association finding of the single nucleotide polymorphism rs17070145 with human memory performance by accumulating information of about 6 years of research performed worldwide. The heritability analysis verified that a common SNP-chip array is an appropriate dataset to perform more complex genetic analysis with human working memory performance measurements. The analysis of common epigenetic variation validates the DNA CpG methylation dataset in the context of complex analyses in mentally healthy young adults. Additionally, when comparing the three analyses, they also shed light on the varying complexity of putative intermediate phenotypes in human research. This knowledge can be used to build up comprehensive theoretical models and complex statistical analyses that combine several complex phenotypes
