title: Modelling developmental instability as the joint action of noise and stability: a Bayesian approach.
abstract: BACKGROUND
  Fluctuating asymmetry is assumed to measure individual and population level developmental stability. The latter may in turn show an association with stress, which can be observed through asymmetry-stress correlations. However, the recent literature does not support an ubiquitous relationship. Very little is known why some studies show relatively strong associations while others completely fail to find such a correlation. We propose a new Bayesian statistical framework to examine these associations
  
  
  RESULTS
  We are considering developmental stability - i.e. the individual buffering capacity - as the biologically relevant trait and show that (i) little variation in developmental stability can explain observed variation in fluctuating asymmetry when the distribution of developmental stability is highly skewed, and (ii) that a previously developed tool (i.e. the hypothetical repeatability of fluctuating asymmetry) contains only limited information about variation in developmental stability, which stands in sharp contrast to the earlier established close association between the repeatability and developmental instability.
  
  
  CONCLUSIONS
  We provide tools to generate valuable information about the distribution of between-individual variation in developmental stability. A simple linear transformation of a previous model lead to completely different conclusions. Thus, theoretical modelling of asymmetry and stability appears to be very sensitive to the scale of inference. More research is urgently needed to get better insights in the developmental mechanisms of noise and stability. In spite of the fact that the model is likely to represent an oversimplification of reality, the accumulation of new insights could be incorporated in the Bayesian statistical approach to obtain more reliable estimation.
authors.affiliation: Global Biometrics and Reporting Janssen Pharmaceutica Beerse, Belgium. svdongen@janbe.jnj.com
authors.forename: Stefan
authors.initials: S
authors.lastname: Van Dongen
authors.forename: Luc
authors.initials: L
authors.lastname: Lens
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D036262:Empirical Research; D014644:Genetic Variation; D005828:Genetics, Population; D042822:Genomic Instability; D008957:Models, Genetic; D010641:Phenotype; D012107:Research Design / Q000706:statistics & numerical data