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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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Graphical Modeling of Biological Pathways in Genome-wide Association Studies

Graphical Modeling of Biological Pathways in Genome-wide Association Studies

Chapter:
(p.294) Chapter 12 Graphical Modeling of Biological Pathways in Genome-wide Association Studies
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Min Chen

Judy Cho

Hongyu Zhao

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0012

Genome-wide association studies (GWASs) are widely used to identify good candidates of disease-associated genes that are of interest for further follow-up studies. However, knowledge of biological pathways and interactions may improve the likelihood of making genuine discoveries in GWASs. A number of methods have been developed to incorporate prior biological knowledge when prioritizing genes. However, most methods treat genes in a specific pathway as an exchangeable set without considering the topological structure of the pathway. Based on results obtained from a standard association study on a Crohn’s disease cohort, it is first verified that neighboring genes in a pathway are more likely to share the same disease status. Then, a Markov Random Field (MRF) model is proposed, to incorporate pathway topology for association analysis. We show that the conditional distribution of our MRF model takes on a simple logistic regression form. Finally, we evaluate our model on real data.

Keywords:   Genome-wide association studies, biological pathways, Markov random fields

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