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Variable selection in Bayesian generalized linear-mixed models: an illustration using candidate gene case-control
1Institute of Statistics and Information Science, National Changhua University of Education, Changhua, 500, Taiwan.
Biometrical Journal. Biometrische Zeitschrift
|October 1, 2014
Summary
This study introduces a new method for selecting important variables in complex statistical models, improving efficiency and reliability in genetic association studies.
Area of Science:
- Statistics
- Genetics
- Computational Biology
Background:
- Variable selection is a critical challenge in generalized linear-mixed models (GLMMs).
- Existing methods for variable selection can be computationally intensive and complex.
- Identifying relevant explanatory variables is essential for accurate statistical modeling.
Purpose of the Study:
- To develop an efficient variable selection approach for GLMMs.
- To reduce the computational burden associated with fitting numerous models.
- To provide a reliable method for identifying candidate genes and gene-gene associations.
Main Methods:
- Developed a "higher posterior probability model with bootstrap" (HPMB) approach.
- Utilized Laplace's method and Taylor's expansion for efficient integral approximation.
- Applied the method to HapMap data for validation.
Main Results:
- The HPMB approach is computationally feasible and reliable.
- Successfully identified true candidate genes and gene-gene associations.
- Demonstrated effectiveness in adjusting for complex clustered data structures.
Conclusions:
- The proposed HPMB method offers an efficient and reliable solution for variable selection in GLMMs.
- This approach is particularly valuable for genetic studies involving complex data.
- The method aids in exploring true candidate genes and gene-gene associations effectively.
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