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Arthritis Research pp 143–176Cite as

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

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Part of the book series: Methods in Molecular Biology ((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.

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Authors and Affiliations

  1. Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary

    Gábor Hullám, András Millinghoffer, Péter Sárközy & Bence Bolgár

  2. Department of Genetics, Cell and Immunobiology, Semmelweis University, Budapest, Hungary

    András Gézsi, Sanjeev K. Srivastava, Zsuzsanna Pál & Edit I. Buzás

  3. Department of Measurement and Information Systems, Budapest University of Technology and Economics (BME), Magyar Tudosok krt.2. Bldg. I, Wing E, Level 4, Room IE444, 1521, Budapest, Hungary

    Péter Antal

Authors
  1. Gábor Hullám
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  2. András Gézsi
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  3. András Millinghoffer
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  4. Péter Sárközy
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  5. Bence Bolgár
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  6. Sanjeev K. Srivastava
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  7. Zsuzsanna Pál
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  8. Edit I. Buzás
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  9. Péter Antal
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Correspondence to Péter Antal .

Editor information

Editors and Affiliations

  1. Department of Rheumatology, Kyushu University Beppu Hospital, Beppu, Japan

    Shunichi Shiozawa

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Hullám, G. et al. (2014). Bayesian Systems-Based Genetic Association Analysis with Effect Strength Estimation and Omic Wide Interpretation: A Case Study in Rheumatoid Arthritis. In: Shiozawa, S. (eds) Arthritis Research. Methods in Molecular Biology, vol 1142. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0404-4_14

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  • DOI: https://doi.org/10.1007/978-1-4939-0404-4_14

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-0403-7

  • Online ISBN: 978-1-4939-0404-4

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