title: Linear genetics, non-linear epigenetics: complementary approaches to understanding complex diseases.
abstract: Recent discoveries and rediscoveries in molecular and cell biology, in population and evolutionary biology, and in disease natural history raise new doubts about the ability of genetic analysis alone to predict multifactorial (polygenic) human diseases and other complex phenotypes. These doubts serve to redirect our attention to epigenetic regulation as a second informational system in parallel with the genome. Epigenetic regulation is now viewed by many biologists as a process that includes mechanisms capable of constraining the genome and providing for new patterns of gene expression. Epigenetic networks, both intra- and inter-cellular, provide a basis for nonlinear and chaotic views of cellular and tissue level differentiation and organization, and thus provide a more dynamic approach to understanding the creation of complex phenotypes, even from isogeneic conditions. The reality of regulatory networks within cells inserted, as it were, between genome and phenome, also helps explain the difficulties now encountered when prediction and diagnosis of complex disease omits epigenetic considerations and depends entirely on gene causality.
authors.affiliation: Dept. of Molecular and Cell Biology, University of California, Berkeley 94720, USA.
authors.forename: R C
authors.initials: RC
authors.lastname: Strohman
mesh_terms: D000818:Animals; D005075:Biological Evolution; D030342:Genetic Diseases, Inborn / Q000235:genetics* / Q000503:physiopathology; D006801:Humans; D008967:Molecular Biology; D017711:Nonlinear Dynamics