What do biological networks reveal about epistasis and pleiotropy?

Our paper on "Shadows of complexity: What biological networks reveal and epistasis and pleiotropy" has been accepted for publication by BioEssays. Anna Tyler in my lab wrote much of this paper with help from myself, Dr. Folkert Asselbergs from Groningen and Dr. Scott Williams from Vanderbilt. The editor, Dr. Adam Wilkins, and the referees played a very important role in helping us shape this paper. One of the issues we addressed was how to define epistasis. The are classicial definitions from the early 1900s and more modern definitions based on what we know about systems biology. This paper builds on our previous paper on statistical vs. biological epistasis that was published in BioEssays in 2005. The link to information about the previous paper in PubMed can be found here. It was mentioned in a February 2005 post on this blog.

Tyler, A., Asselbergs, F.A., Williams, S.M., Moore, J.H. Shadows of complexity: What biological networks reveal and epistasis and pleiotropy. BioEssays, in press (2009).

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.