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

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)143-176
Number of pages34
JournalMethods in Molecular Biology
Volume1142
DOIs
Publication statusPublished - Jan 1 2014

Keywords

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

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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