Behavior Genetics

, Volume 33, Issue 3, pp 279–299

Markov Chain Monte Carlo Approaches to Analysis of Genetic and Environmental Components of Human Developmental Change and G × E Interaction

  • Lindon Eaves
  • Alaattin Erkanli

DOI: 10.1023/A:1023446524917

Cite this article as:
Eaves, L. & Erkanli, A. Behav Genet (2003) 33: 279. doi:10.1023/A:1023446524917


The linear structural model has provided the statistical backbone of the analysis of twin and family data for 25 years. A new generation of questions cannot easily be forced into the framework of current approaches to modeling and data analysis because they involve nonlinear processes. Maximizing the likelihood with respect to parameters of such nonlinear models is often cumbersome and does not yield easily to current numerical methods. The application of Markov Chain Monte Carlo (MCMC) methods to modeling the nonlinear effects of genes and environment in MZ and DZ twins is outlined. Nonlinear developmental change and genotype × environment interaction in the presence of genotype-environment correlation are explored in simulated twin data. The MCMC method recovers the simulated parameters and provides estimates of error and latent (missing) trait values. Possible limitations of MCMC methods are discussed. Further studies are necessary explore the value of an approach that could extend the horizons of research in developmental genetic epidemiology.

Growth curves Bayesian inference Gibbs sampling Markov Chain Monte Carlo methods twins longitudinal studies G × E interaction, hierarchical mixed models 

Copyright information

© Plenum Publishing Corporation 2003

Authors and Affiliations

  • Lindon Eaves
  • Alaattin Erkanli

There are no affiliations available