Fitting genetic models to twin data with binary and ordered categorical responses: a comparison of structural equation modelling and Bayesian hierarchical models.
We compare Bayesian methodology utilizing free-ware BUGS (Bayesian Inference Using Gibbs Sampling) with the traditional structural equation modelling approach based on another free-ware package, Mx. Dichotomous and ordinal (three category) twin data were simulated according to different additive genetic and common environment models for phenotypic variation. Practical issues are discussed in using Gibbs sampling as implemented by BUGS to fit subject-specific Bayesian generalized linear models, where the components of variation may be estimated directly. The simulation study (based on 2000 twin pairs) indicated that there is a consistent advantage in using the Bayesian method to detect a "correct" model under certain specifications of additive genetics and common environmental effects. For binary data, both methods had difficulty in detecting the correct model when the additive genetic effect was low (between 10 and 20%) or of moderate range (between 20 and 40%). Furthermore, neither method could adequately detect a correct model that included a modest common environmental effect (20%) even when the additive genetic effect was large (50%). Power was significantly improved with ordinal data for most scenarios, except for the case of low heritability under a true ACE model. We illustrate and compare both methods using data from 1239 twin pairs over the age of 50 years, who were registered with the Australian National Health and Medical Research Council Twin Registry (ATR) and presented symptoms associated with osteoarthritis occurring in joints of the hand.
The Ecology Centre, The University of Queensland, St Lucia, Queensland 4072, Australia. p.kuhnert@zen.uq.edu.au
Petra M
PM
Kuhnert
Kim-Anh
KA
Do
D001499:Bayes Theorem; D016208:Databases, Factual; D006801:Humans; D008875:Middle Aged; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method