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Summarizing and quantifying multilocus linkage disequilibrium patterns with multi-order Markov chain models.

Sheng Feng1, Shengchu Wang

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA. sheng.feng@duke.edu

Journal of Biopharmaceutical Statistics
|March 24, 2010
PubMed
Summary
This summary is machine-generated.

This study links multilocus linkage disequilibrium (LD) patterns to a novel Markov chain model. Mathematical derivations and simulations help understand LD complexity for disease gene mapping.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Computational biology

Background:

  • Linkage disequilibrium (LD) is crucial for mapping disease genes.
  • Multilocus LD patterns reflect complex genetic architectures.
  • A novel multi-order Markov chain model quantifies multilocus LD complexity.

Purpose of the Study:

  • To derive mathematical relationships between Markov chain model parameters and conventional LD measures.
  • To understand the Markov chain model parameters in terms of established LD metrics.
  • To investigate the statistical properties of Markov chain order estimates via simulations.

Main Methods:

  • Derivation of mathematical relationships between LD measures.
  • Simulation studies to assess statistical properties of Markov chain order estimates.
  • Reanalysis of two published datasets using the proposed approach.

Main Results:

  • Established mathematical links between the Markov chain model and conventional LD measures.
  • Investigated the statistical sample properties of the Markov chain order estimates.
  • Demonstrated the utility of the approach through reanalysis of existing data.

Conclusions:

  • The study provides a framework for understanding complex LD patterns using a Markov chain model.
  • The findings facilitate the application of the Markov chain model in genetic studies.
  • This work enhances the interpretation of LD in the context of disease gene mapping.