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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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Bayesian, Systems-based, Multilevel Analysis of Associations for Complex Phenotypes: from Interpretation to Decision

Bayesian, Systems-based, Multilevel Analysis of Associations for Complex Phenotypes: from Interpretation to Decision

Chapter:
(p.318) Chapter 13 Bayesian, Systems-based, Multilevel Analysis of Associations for Complex Phenotypes: from Interpretation to Decision
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Péter Antal

András Millinghoffer

Gábor Hullám

Gergely Hajós

Péter Sárközy

András Gézsi

Csaba Szalai

András Falus

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0013

The relative scarcity of the results reported by genetic association studies (GAS) prompted many research directions. Despite the centrality of the concept of association in GASs, refined concepts of association are missing; meanwhile, various feature subset selection methods became de facto standards for defining multivariate relevance. On the other hand, probabilistic graphical models, including Bayesian networks (BNs) are more and more popular, as they can learn nontransitive, multivariate, nonlinear relations between complex phenotypic descriptors and heterogeneous explanatory variables. To integrate the advantages of Bayesian statistics and BNs, the Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA) was proposed. This approach allows the processing of multiple target variables, while ensuring scalability and providing a multilevel view of the results of multivariate analysis. This chapter discusses the use of Bayesian BN-based analysis of relevance in exploratory data analysis, optimal decision and study design, and knowledge fusion, in the context of GASs.

Keywords:   genome-wide association studies, Bayesian network, relevance

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