A note on genetic variance components in mixed models.
Burton et al. ([1999] Genet. Epidemiol. 17:118-140) proposed a series of generalized linear mixed models for pedigree data that account for residual correlation between related individuals. These models may be fitted using Markov chain Monte Carlo methods, but the posterior mean for small variance components can exhibit marked positive bias. Burton et al. ([1999] Genet. Epidemiol. 17:118-140) suggested that this problem could be overcome by allowing the variance components to take negative values. We examine this idea in depth, and show that it can be interpreted as a computational device for locating the posterior mode without necessarily implying that the original random effects structure is incorrect. We illustrate the application of this technique to mixed models for familial data.
Hazelton
Martin L
ML
School of Mathematics and Statistics, University of Western Australia, Crawley, Western Australia, Australia. martin@maths.uwa.edu.au
Gurrin
Lyle C
LC
D003627:Data Interpretation, Statistical; D014644:Genetic Variation; D040481:Genome Components; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical