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An efficient technique for Bayesian modeling of family data using the BUGS software.

Harold T Bae1, Thomas T Perls2, Paola Sebastiani3

  • 1School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University Corvallis, OR, USA.

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|December 6, 2014
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This study introduces an efficient Bayesian method for analyzing family data using linear mixed models. The new parameterization simplifies complex covariance matrices, improving analysis with Bayesian inference Using Gibbs Sampling (BUGS) software.

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Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Genetics

Background:

  • Linear mixed models are widely used for analyzing correlated data in family studies.
  • Implementing these models in Bayesian inference Using Gibbs Sampling (BUGS) is challenging due to high-dimensional covariance matrices.
  • Existing methods may struggle with the complexity of family-based data structures.

Purpose of the Study:

  • To present an efficient parameterization for linear mixed models in family-based designs using BUGS.
  • To extend this parameterization to generalized linear mixed models.
  • To provide practical BUGS code for implementing the proposed method.

Main Methods:

  • Utilized singular value decomposition (SVD) of the random effects covariance matrix.
  • Developed a novel parameterization for efficient implementation in BUGS.
  • Extended the approach to generalized linear mixed models for non-continuous data.

Main Results:

  • The proposed parameterization significantly simplifies the implementation of mixed models for family data in BUGS.
  • The method was successfully evaluated using simulated datasets.
  • Demonstrated the practical application and compared performance with existing methods on a large family study dataset.

Conclusions:

  • The SVD-based parameterization offers an efficient and effective approach for Bayesian analysis of family data.
  • This method facilitates the use of BUGS for complex correlated data structures.
  • The findings extend the applicability of mixed models to generalized linear models within family-based research.