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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Using object oriented bayesian networks to model linkage, linkage disequilibrium and mutations between STR markers.

Daniel Kling1, Thore Egeland, Petter Mostad

  • 1Department of Family Genetics, Norwegian Institute of Public Health, Oslo, Norway. daniel.kling@fhi.no

Plos One
|September 18, 2012
PubMed
Summary

This study presents a Bayesian Network framework for analyzing genetic marker data in pedigree analysis. It highlights that while linkage disequilibrium (LD) and linkage are often ignored, they can significantly impact complex relatedness cases, unlike simple paternity testing.

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Area of Science:

  • Forensic Genetics
  • Statistical Genetics
  • Computational Biology

Background:

  • Determining familial relationships using genetic markers is crucial in various applications.
  • Existing models often overlook the complexities of marker linkage and population linkage disequilibrium (LD).

Purpose of the Study:

  • To introduce a graphical Bayesian Network framework for analyzing genetic marker data, accounting for linkage and LD.
  • To assess the impact of ignoring linkage and LD on pedigree analysis and likelihood ratio calculations.
  • To demonstrate the integration of mutation models within the Bayesian Network framework.

Main Methods:

  • Development of a graphical Bayesian Network model.
  • Estimation of population haplotype probabilities to account for LD using trio data.
  • Inclusion of a mutation model within the Bayesian Network.
  • Application to two STR markers (vWa and D12S391) as a case study.

Main Results:

  • For simple paternity cases with the vWa and D12S391 markers, statistical independence can often be assumed.
  • Ignoring LD and linkage can lead to biased likelihood ratios in more complex relatedness cases (e.g., siblings, deficient cases).
  • The proposed Bayesian Network readily incorporates mutation models, simplifying their inclusion compared to other methods.

Conclusions:

  • The Bayesian Network framework provides a robust method for pedigree analysis with genetic markers, accommodating linkage and LD.
  • The impact of LD and linkage is context-dependent, being minimal in standard paternity cases but significant in complex scenarios.
  • The framework offers a practical approach to incorporating mutation models, enhancing the accuracy of relatedness assessments.