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Bayesian Systems-Based Genetic Association Analysis with Effect Strength Estimation and Omic Wide Interpretation: A Case Study in Rheumatoid Arthritis

  • Gábor Hullám
  • András Gézsi
  • András Millinghoffer
  • Péter Sárközy
  • Bence Bolgár
  • Sanjeev K. Srivastava
  • Zsuzsanna Pál
  • Edit I. Buzás
  • Péter Antal
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1142)

Abstract

Rich dependency structures are often formed in genetic association studies between the phenotypic, clinical, and environmental descriptors. These descriptors may not be standardized, and may encompass various disease definitions and clinical endpoints which are only weakly influenced by various (e.g., genetic) factors. Such loosely defined complex intermediate clinical phenotypes are typically used in follow-up candidate gene association studies, e.g., after genome-wide analysis, to deepen the understanding of the associations and to estimate effect strength.

This chapter discusses a solid methodology, which is useful in such a scenario, by using probabilistic graphical models, namely, Bayesian networks in the Bayesian statistical framework. This method offers systematically scalable, comprehensive hierarchical hypotheses about multivariate relevance. We discuss its workflow: from data engineering to semantic publication of the results. We overview the construction, visualization, and interpretation of complex hypotheses related to the structural analysis of relevance. Furthermore, we illustrate the use of a dependency model-based relevance measure, which takes into account the structural properties of the model, for quantifying the effect strength. Finally, we discuss the “interpretational” or translational challenge of a genetic association study, with a focus on the fusion of heterogeneous omic knowledge to reintegrate the results into a genome-wide context.

Key words

Genetic association studies Detailed phenotyping Bayesian networks Bayesian multilevel analysis of variance Bayesian structure-based effect strength estimation Gene prioritization 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Gábor Hullám
    • 1
  • András Gézsi
    • 2
  • András Millinghoffer
    • 1
  • Péter Sárközy
    • 1
  • Bence Bolgár
    • 1
  • Sanjeev K. Srivastava
    • 2
  • Zsuzsanna Pál
    • 2
  • Edit I. Buzás
    • 2
  • Péter Antal
    • 3
  1. 1.Department of Measurement and Information SystemsBudapest University of Technology and EconomicsBudapestHungary
  2. 2.Department of Genetics, Cell and ImmunobiologySemmelweis UniversityBudapestHungary
  3. 3.Department of Measurement and Information SystemsBudapest University of Technology and Economics (BME)BudapestHungary

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