Shadows of complexity: what biological networks reveal about epistasis and pleiotropy

Abstract

Pleiotropy, in which one mutation causes multiple phenotypes, has traditionally been seen as a deviation from the conventional observation in which one gene affects one phenotype. Epistasis, or gene-gene interaction, has also been treated as an exception to the Mendelian one gene-one phenotype paradigm. This simplified perspective belies the pervasive complexity of biology and hinders progress toward a deeper understanding of biological systems. We assert that epistasis and pleiotropy are not isolated occurrences, but ubiquitous and inherent properties of biomolecular networks. These phenomena should not be treated as exceptions, but rather as fundamental components of genetic analyses. A systems level understanding of epistasis and pleiotropy is, therefore, critical to furthering our understanding of human genetics and its contribution to common human disease. Finally, graph theory offers an intuitive and powerful set of tools with which to study the network bases of these important genetic phenomena.

Figure 1

Examples of node connectivity in random and scale-free networks. (A) In a random network, the connectivity of the nodes follows a Gaussian distribution. A plot of log k against log P(k) follows a Gaussian distribution.(B) In a scale-free network most nodes (light gray) are connected to one or two other nodes, while a few hubs (dark gray) are connected to many other nodes. In a random network, a plot of log k against log P(k) follows a Gaussian distribution. In a scale-free network, a plot of log k against log P(k) follows a straight line following the power law P(k) = Akˆ-3.

Figure 2

This figure summarizes gene-gene interactions and pleiotropic effects found statistically in a series of studies in humans.(60-64) The genes and polymorphisms examined in the studies are listed in the center row, and the phenotypes they affect are listed on the top and bottom. Downward arrows represent significant pairwise epistatic interactions between polymorphisms that affect the two phenotypes, in this case plasma levels of t-PA and PAI-1. Upward arrows in Figure 2 indicate significant main effects of each polymorphism on each phenotype. AT1R had a significant main effect on both t-PA and PAI-1. Here the angiotensin receptor is shown to be pleiotropic as it influences levels of both t-PA and PAI-1.