To assist in understanding the complex behavior of WIS, the enterprise architecture approaches provide assistance, namely the Zachman ontology and TOGAF, both were developed for information systems [24, 25, 31].
Our findings indicate that we are currently looking at the tip of the iceberg: the focus should now be placed on the development of innovative experimental and computational strategies to deepen our understanding of the complex architecture of quantitative traits.
Integration of multidisciplinary approaches such as plant physiology, developmental biology, biophysics and modelling not only could help in understanding the complex mechanism of hyponasty, but also any other alterations of plant architecture resulting from diverse external stresses.
Recently, partitioning the heritability accounted for by common variants into the contributions of genomic regions has received a lot of attention given its important applications for understanding the genetic architecture of complex traits.
For all these reasons we believe fly models will prove useful in understanding the genetic architecture of complex human disease.
With increased availability of various -omics data, integrative approaches are promising tools for understanding the genetic architecture of complex traits.
In any case, systems mapping will open a new avenue for understanding the genetic architecture of complex phenotypes from a perspective of mechanistic pathways inside their formation.
One of the main limitations to better understanding the genetic architecture of complex traits is that typically the data structure does not allow simultaneous estimation of additive, dominance, and imprinting variance (De Vries et al. 1994; Vitezica et al. 2013).
Elucidation of the pig transcriptome is essential for interpreting functional elements of the genome and understanding the genetic architecture of complex traits such as fat deposition, metabolism and growth.
The identification of genes that have coevolved and of artificial selective sweeps in rat disease models may therefore be informative for identifying loci underlying disease phenotypes and for understanding the polygenic architecture of complex disease phenotypes.
