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Related Experiment Videos

Evaluation of likelihood ratios for complex genetic models.

E A Thompson1, S W Guo

  • 1Department of Statistics, University of Washington, Seattle 98195.

IMA Journal of Mathematics Applied in Medicine and Biology
|January 1, 1991
PubMed
Summary

This study introduces the Gibbs sampler for Monte Carlo likelihood evaluation in complex genetic models on extended pedigrees. This computational method makes complex genetic analyses, including linkage and segregation analysis, more tractable and efficient.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Computational Biology

Background:

  • Existing methods for genetic likelihood computation are limited for complex models on large or extended pedigrees.
  • Likelihood evaluation for complex genetic models on extended pedigrees has been computationally intractable.
  • Advances in computing power necessitate efficient methods for complex pedigree analyses.

Purpose of the Study:

  • To present the Gibbs sampler as a method for Monte Carlo evaluation of likelihood ratios in complex genetic models.
  • To demonstrate the application of the Gibbs sampler for likelihood evaluation on extended and complex pedigrees.
  • To show how this framework accommodates linkage analysis for quantitative traits.

Main Methods:

  • Sequential computation of Gaussian likelihoods for multiple random-effects models on extended pedigrees.

Related Experiment Videos

  • Monte Carlo evaluation of likelihoods for the classical mixed model of segregation analysis using the Gibbs sampler.
  • Detailed implementation of the Gibbs sampler on pedigrees for Monte Carlo evaluation.

    • Main Results:

    • The Gibbs sampler provides a computationally tractable and efficient approach to likelihood evaluation for complex genetic models on extended pedigrees.
    • The proposed method successfully handles complex genetic models and extended pedigree structures.
    • Linkage analysis for quantitative traits can be integrated within this Gibbs sampling framework.

    Conclusions:

    • The Gibbs sampler offers a powerful tool for overcoming computational challenges in complex genetic analyses.
    • This approach enhances the feasibility of segregation and linkage analyses on large and complex pedigree data.
    • The method's efficiency is expected to increase with advancements in computer processing speeds.