Genotype to phenotype via network analysis

Abstract

A prime objective of genomic medicine is the identification of disease-causing mutations and the mechanisms by which such events result in disease. As most disease phenotypes arise not from single genes and proteins but from a complex network of molecular interactions, a priori knowledge about the molecular network serves as a framework for biological inference and data mining. Here we review recent developments at the interface of biological networks and mutation analysis. We examine how mutations may be treated as a perturbation of the molecular interaction network and what insights may be gained from taking this perspective. We review work that aims to transform static networks into rich context-dependent networks and recent attempts to integrate non-coding RNAs into such analysis. Finally, we conclude with an overview of the many challenges and opportunities that lie ahead.

Figure 1. A hierarchical perspective of biological interactions mediating genotype-phenotype relationships

Protein activity is determined by protein amino acid sequence and structure. Proteins contribute to biological processes through interactions with other molecules in the cell. Biological processes arise from coordinated groups of molecular interactions, and in turn can interact to mediate higher order cellular behaviors and responses to environmental cues. Advances in several areas of network research are improving our understanding of how the organization of biological systems mediates genotype-phenotype relationships. This knowledge will be essential for identifying mutations underlying disease associations and their mechanisms of pathogenesis.

Figure 2. Hierarchical representations provide interpretable views of how molecular networks contribute to biological function

The subnetwork comprising interactions among proteins of the S. cerevisiae proteasome is depicted using different network layouts: (a) hierarchical (b) force-directed and (c) a layout showing within-module edges and between-module edges. In the hierarchical representation, distal nodes are included in proximal nodes (for example the node labeled “core” encapsulates the alpha and beta subunits depicted in red and cyan). Node size corresponds to the number of genes participating in the term and node color gives degree of correspondence to annotated biological activities. Branches and nodes corresponding to physical complexes with known biological function are labeled. Node colors in panels (b) and (c) match the complexes highlighted in panel (a). Reproduced with permission from Dutkowski et al. [90**].