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Estimation of graphical models whose conditional independence graphs are interval graphs and its application to
1University of Utah.
Computational Statistics & Data Analysis
|February 18, 2010
Summary
This study compares graphical model estimation methods for genetic data. Restricting graphs to interval graphs improves model search efficiency with minimal impact on haplotype frequency estimates.
Area of Science:
- Computational biology
- Statistical genetics
- Graph theory
Background:
- Graphical models are used to estimate genetic associations.
- Decomposable graphs offer a general framework, while interval graphs provide a more restrictive assumption.
Purpose of the Study:
- To compare the estimation of graphical models under decomposable graphs versus interval graphs.
- To assess the impact of interval graph restrictions on Markov chain Monte Carlo (MCMC) search and haplotype frequency estimation.
- To explore further restrictions for modeling allele associations and population haplotype frequencies.
Main Methods:
- Comparison of graphical model estimation techniques.
- Utilizing Markov chain Monte Carlo (MCMC) search.
- Analyzing the effects of interval graph restrictions on model search and haplotype frequencies.
Main Results:
- Restricting graphs to interval graphs improves MCMC search efficiency.
- This restriction has minimal effect on estimated haplotype frequencies.
- Further restrictions are suitable for modeling allele associations at genetic loci.
Conclusions:
- Interval graph restrictions offer an efficient approach for estimating graphical models in genetics.
- These methods enhance the description of association patterns and population haplotype frequencies.
- The findings are applicable to statistical gene mapping, including linkage and association studies.
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