title: Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis.
abstract: Epistasis plays an important role in the genetic architecture of common human diseases and can be viewed from two perspectives, biological and statistical, each derived from and leading to different assumptions and research strategies. Biological epistasis is the result of physical interactions among biomolecules within gene regulatory networks and biochemical pathways in an individual such that the effect of a gene on a phenotype is dependent on one or more other genes. In contrast, statistical epistasis is defined as deviation from additivity in a mathematical model summarizing the relationship between multilocus genotypes and phenotypic variation in a population. The goal of this essay is to review definitions and examples of biological and statistical epistasis and to explore the relationship between the two. Specifically, we present and discuss the following two questions in the context of human health and disease. First, when does statistical evidence of epistasis in human populations imply underlying biomolecular interactions in the etiology of disease? Second, when do biomolecular interactions produce patterns of statistical epistasis in human populations? Answers to these two reciprocal questions will provide an important framework for using genetic information to improve our ability to diagnose, prevent and treat common human diseases. We propose that systems biology will provide the necessary information for addressing these questions and that model systems such as bacteria, yeast and digital organisms will be a useful place to start.
authors.affiliation: Department of Genetics, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, NH, USA. jason.h.moore@dartmouth.edu
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.forename: Scott M
authors.initials: SM
authors.lastname: Williams
mesh_terms: D000818:Animals; D004843:Epistasis, Genetic; D006801:Humans; D008954:Models, Biological; D049490:Systems Biology