st: RE: mixed models -- possibility to fix elements of the error variance matrix?

[David Airey <david.airey@Vanderbilt.Edu>]

The mixed or multilevel model for behavior genetic analysis.

Guo G, Wang J.

Department of Sociology, University of North Carolina at Chapel Hill,  
27599, USA.

We propose the mixed model or multilevel model as a general  
alternative approach
to existing behavior genetic analysis-an alternative to correlation  
analysis, the
DeFries-Fulker analysis, and structural equation modeling. The mixed or
multilevel model handles readily families of behavioral genetic data,  
which
include paired sibling data (e.g., pairs of MZ and DZ twins) and  
clustered
sibling data (e.g., a family of more than two biological siblings) as  
special
cases. Not only can a family of behavioral genetic data have more  
than two
siblings, it can also contain multiple types of siblings (e.g., a  
pair of MZ
twins, a pair of DZ twins, a full sibling, and a half sibling). In  
contrast to
the traditional approaches, the mixed or multilevel model is  
insensitive to the
order of the siblings in a sibling cluster. We apply our approach to  
a large,
nationally representative behavior genetic sample collected recently  
by the Add
Health Study. We demonstrate the approach through several  
applications using both
clustered and family complex behavioral genetic data: conventional  
variance
decomposition analysis, analysis of interactions between genetic and
environmental influences, and analysis of the possible genetic basis for
friendship selection. We compare results from the mixed or multilevel  
model,
Pearson's correlation analysis, and the structural equation model.

Publication Types:
    Research Support, Non-U.S. Gov't
    Research Support, U.S. Gov't, Non-P.H.S.
    Twin Study

PMID: 11958541 [Pubmed - indexed for MEDLINE]


--
David C. Airey, Ph.D.
Research Assistant Professor


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