Premium
A note on genetic variance components in mixed models
Author(s) -
Hazelton Martin L.,
Gurrin Lyle C.
Publication year - 2003
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.10242
Subject(s) - markov chain monte carlo , variance (accounting) , variance components , mathematics , markov chain , statistics , mixed model , random effects model , residual , series (stratigraphy) , monte carlo method , correlation , econometrics , statistical physics , algorithm , biology , physics , medicine , paleontology , meta analysis , geometry , accounting , business
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. Genet Epidemiol 24:297–301, 2003. © 2003 Wiley‐Liss, Inc.