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Fitting Genetic Models Using Markov Chain Monte Carlo Algorithms With BUGS

Published online by Cambridge University Press:  21 February 2012

Stéphanie M. van den Berg ,
Leo Beem  and
Dorret I. Boomsma
Stéphanie M. van den Berg*
Affiliation:
Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands. [email protected]
Leo Beem
Affiliation:
Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands.
Dorret I. Boomsma
Affiliation:
Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands.
*
*Address for correspondence: S. M. van den Berg, Vrije Universiteit Amsterdam, Department of Biological Psychology, Van der Boechorststraat 1, PO Box 1081 BT, Amsterdam, the Netherlands.

Abstract

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Maximum likelihood estimation techniques are widely used in twin and family studies, but soon reach computational boundaries when applied to highly complex models (e.g., models including gene-by-environment interaction and gene–environment correlation, item response theory measurement models, repeated measures, longitudinal structures, extended pedigrees). Markov Chain Monte Carlo (MCMC) algorithms are very well suited to fit complex models with hierarchically structured data. This article introduces the key concepts of Bayesian inference and MCMC parameter estimation and provides a number of scripts describing relatively simple models to be estimated by the freely obtainable BUGS software. In addition, inference using BUGS is illustrated using a data set on follicle-stimulating hormone and luteinizing hormone levels with repeated measures. The examples provided can serve as stepping stones for more complicated models, tailored to the specific needs of the individual researcher.

Type
Special Section on Advances in Statistical Models and Methods
Information
Twin Research and Human Genetics , Volume 9 , Issue 3 , 01 June 2006 , pp. 334 - 342
Copyright
Copyright © Cambridge University Press 2006