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Related Experiment Videos

A note on genetic variance components in mixed models.

Martin L Hazelton1, Lyle C Gurrin

  • 1School of Mathematics and Statistics, University of Western Australia, Crawley, Western Australia, Australia. martin@maths.uwa.edu.au

Genetic Epidemiology
|April 11, 2003
PubMed
Summary
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Generalized linear mixed models for pedigree data can have bias in variance components. Allowing negative values is a computational method to find the posterior mode, not an indication of incorrect model structure.

Area of Science:

  • Statistics
  • Genetics
  • Epidemiology

Background:

  • Generalized linear mixed models (GLMMs) are used for analyzing correlated data, such as in genetic epidemiology.
  • Previous work by Burton et al. (1999) proposed GLMMs for pedigree data to account for residual correlation.
  • A known issue is the positive bias in posterior means for small variance components when using Markov chain Monte Carlo (MCMC) methods.

Purpose of the Study:

  • To investigate the suggestion of allowing variance components to take negative values to overcome bias in GLMMs for pedigree data.
  • To provide a deeper understanding of this technique's interpretation and application.
  • To demonstrate its utility in analyzing familial data.

Main Methods:

  • Examination of generalized linear mixed models (GLMMs) applied to pedigree data.

Related Experiment Videos

  • Analysis of Markov chain Monte Carlo (MCMC) methods for fitting these models.
  • Theoretical exploration of allowing negative variance components as a computational strategy.

    • Main Results:

    • The strategy of allowing negative variance components can be interpreted as a computational device for locating the posterior mode.
    • This approach does not necessarily imply that the underlying random effects structure of the model is incorrect.
    • The technique was successfully illustrated in the context of mixed models for familial data.

    Conclusions:

    • Allowing negative variance components is a valuable computational tool for fitting GLMMs to pedigree data.
    • This method aids in finding the posterior mode, addressing bias issues without invalidating the model's random effects.
    • The findings offer practical insights for researchers analyzing correlated familial data using mixed models.