title: Dynamic models of biological pattern formation have some surprising implications for understanding the epigenetics of development.
abstract: Nonlinear epigenetic processes are conceived of in terms of self-organizing dynamic models of biological pattern formation. Epigenetic processes thus conceived generate substantial subject-specific structural variation, for instance, in growing brain networks. It is shown that standard quantitative genetic modeling based on analyses of interindividual phenotypic variation misclassifies the variation generated by nonlinear epigenetic processes as being due to specific environmental influences. A new quantitative genetic model, iFACE, is introduced to correctly identify the structural variation generated by self-organizing epigenetic processes. iFACE is based on time series analysis of intraindividual variation of a single pair of genetically related subjects. The results of an application of iFACE to multilead EEG obtained with a single dizygotic twin pair is presented.
authors.affiliation: Department of Human Studies and Family Development, The Pennsylvania State University, University Park, PA, USA. pxm21@psu.edu
authors.forename: Peter C M
authors.initials: PC
authors.lastname: Molenaar
authors.forename: Lawrence
authors.initials: L
authors.lastname: Lo
mesh_terms: D015509:Developmental Biology / Q000379:methods*; D044127:Epigenesis, Genetic / Q000502:physiology*; D057890:Epigenomics / Q000379:methods*; D059647:Gene-Environment Interaction; D006801:Humans; D008954:Models, Biological; D011582:Psychological Theory; D013997:Time Factors